• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.391-399
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• 2021

The conformal array sensor has different sub-array depending on different beam steering directions. According to the method to effective the sensor, the performance of the conformal array sensor can be different, where the sub-array selects an effective sensor. Also, due to the figure of the conformal array sensor, the figure of the sub-array can be different each other, which results in different performance on directivity index, beam width and etc. In this paper, two methods to select sub-array which is the criteria for each sensors position vector and directive vector were proposed. For two sub-array selection methods, the performance of the directivity index, horizontal and vertical beam width were compared with the average and variance. In addition, this comparison was conducted when the number of sensors was fixed. When the number of sensors was not fixed, the directional vector method mainly results in high performance, but the performance of vertical beam width was lower or equal. When the number of sensors was fixed, the performance of two methods is similar, but the performance of variance was deteriorated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3544-3564
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• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.11-17
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• 2019

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by $45^{\circ}$ increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users' moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.

• Interaction and multiscale mechanics
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• v.1 no.3
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• pp.357-368
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• 2008

The result of damage detection from on-site measurements is commonly polluted by unavoidable measurement noises. It is widely recognized that this side influence could be reduced to some extent if the sensor placement was properly designed. Although many methods have been proposed to find the optimal number and location of mono-type sensors, the optimal layout of multi-type sensors need further investigation, because a network of heterogeneous sensors is commonly used in engineering. In this paper, a new criterion of the optimal placement for different types of sensors is proposed. A corresponding heuristic is developed to search for good results. In addition, Monte Carlo simulation is suggested to design a robust damage detection system which contains certain redundancies. The validity of these methods is illustrated by two bridge examples.

• Smart Structures and Systems
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• v.18 no.3
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• pp.389-403
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• 2016

Structural deflection can be identified from measured strains from long gague sensors, but the sensor layout scheme greatly influences on the accuracy of identified resutls. To determine the optimal sensor layout scheme for accurate deflection identification of the tied arch bridge, the method of optimal layout of long-gauge fiber optic sensors is studied, in which the characteristic curve is first developed by using the bending macro-strain curve under multiple target load conditions, then optimal sensor layout scheme with different number of sensors are determined. A tied arch bridge is studied as an example to verify the effectiveness and robustness of the proposed method for static and dynamic deflection identification.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.3
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• pp.272-278
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• 2010

A method for damage detection in a plate structure is presented based on strain waves that are generated by impact or damage in the structure. Strain responses from continuous sensors, which are long ribbon-like sensors made from piezoceramic fibers or other materials, were used with a neural network technique to estimate the damage location. The continuous sensor uses only a small number of channels of data acquisition and can cover large areas of the structure. A grid type structural neural system composed of the continuous sensors was developed for effective damage localization in a plate structure. The ratios of maximum strains and arrival times of the maximum strains obtained from the continuous sensors were used as input data to a neural network. Simulated damage localizations on a plate were carried out and the identified damage locations agreed reasonably well with the exact damage locations.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.641-659
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• 2010

Structural Health Monitoring (SHM) gradually becomes a technique for ensuring the health and safety of civil infrastructures and is also an important approach for the research of the damage accumulation and disaster evolving characteristics of civil infrastructures. It is attracting prodigious research interests and the active development interests of scientists and engineers because a great number of civil infrastructures are planned and built every year in mainland China. In a SHM system the sheer number of accompanying wires, fiber optic cables, and other physical transmission medium is usually prohibitive, particularly for such structures as offshore platforms and long-span structures. Fortunately, with recent advances in technologies in sensing, wireless communication, and micro electro mechanical systems (MEMS), wireless sensor technique has been developing rapidly and is being used gradually in the SHM of civil engineering structures. In this paper, some recent advances in the research, development, and implementation of wireless sensors for the SHM of civil infrastructures in mainland China, especially in Dalian University of Technology (DUT) and Harbin Institute of Technology (HIT), are introduced. Firstly, a kind of wireless digital acceleration sensors for structural global monitoring is designed and validated in an offshore structure model. Secondly, wireless inclination sensor systems based on Frequency-hopping techniques are developed and applied successfully to swing monitoring of large-scale hook structures. Thirdly, wireless acquisition systems integrating with different sensing materials, such as Polyvinylidene Fluoride(PVDF), strain gauge, piezoresistive stress/strain sensors fabricated by using the nickel powder-filled cement-based composite, are proposed for structural local monitoring, and validating the characteristics of the above materials. Finally, solutions to the key problem of finite energy for wireless sensors networks are discussed, with future works also being introduced, for example, the wireless sensor networks powered by corrosion signal for corrosion monitoring and rapid diagnosis for large structures.

• Journal of KIISE
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• v.42 no.8
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• pp.1031-1038
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• 2015

The recent emergence of smart phones, wearable devices, and even the IoT concept made it possible for various objects to interact one another anytime and anywhere. Among many of such smart services, a smart home service typically requires a large number of sensors to recognize the residents' activities. For this reason, the ideas on activity recognition using the data obtained from those sensors are actively discussed and studied these days. Furthermore, plenty of sensors are installed in order to recognize activities and analyze their patterns via data mining techniques. However, if many of these sensors should be installed for IoT smart home service, it raises the issue of cost and energy consumption. In this paper, we proposed a new method for reducing the number of sensors for activity recognition in a smart environment, which utilizes the principal component analysis and clustering techniques, and also show the effect of improvement in terms of the activity recognition by the proposed method.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.2
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• pp.115-124
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• 2024

To detect and prevent structural damage caused by various loads on marine structures and ships, structural health monitoring procedure is essential. Estimating loads acting on the structures which are measured by sensors that are mounted properly are crucial for structural health monitoring. However, attaching an excessive number of sensors to the structure without consideration can be inefficient due to the high costs involved and the potential for inducing structural instability. In this study, we introduce a method to determine the optimal number of sensors and their optimized locations for strain measurement sensors, allowing for accurate load estimation throughout the structure using model expansion method. To estimate the loads exerted on the entire structure with minimal sensors, we construct a strain-load interpolation matrix using the strain mode shapes of the finite element (FE) model and select the optimal sensor locations by applying D-Optimal Design and the row exchange algorithm. Finally, we estimate the loads exerted on the entire structure using the model expansion method. To validate the proposed method, we compare the results obtained by applying the optimal sensor placement and model expansion method to an FE model subjected to arbitrary loads with the loads exerted on the entire FE model, demonstrating efficiency and accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.675-682
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• 2021

In an environment in which a limited type and number of sensors are used, a demand for acquiring various context information may appear. In this study, a new method for acquiring various context information than before was proposed in an environment in which a limited number of sensors are required. To this end, a clue was obtained from the Exon-Intron theory, which is gaining great interest in the field of biology, and a method for acquiring various context information was proposed based on this. By applying Exon-Intron's selective cutting and combining method, events of each sensor were efficiently cut and each event data was combined and utilized, thereby realizing the diversification of the acquired context information.