• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.200-207
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• 2024

A multi-component force sensor has been developed to measure force and moment components in high-speed flow media for submerged models. The size of the test model is determined based on the Reynolds number of the model at the test speed and expected blockage effect. A two-component force sensor unit has been created by assembling pairs of column elements arranged symmetrically under an eccentric load. The six-component force sensor is constructed with symmetric arrangements of two-component force sensor units in a rectangular plane. The signals generated from the strain gauges attached to the surface of the elements can be converted into force signals. The performance of the waterproof six-component force sensor has been evaluated through calibration. A simplified interference decomposition procedure has been introduced to increase the accuracy of measurement.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.104-109
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• 2017

We propose a new machine learning algorithm which analyzes daily activity patterns of users from multi-modal wearable sensor data. The proposed model learns and extracts activity patterns using input from wearable devices in real-time. Inspired by cue integration of human's property, we constructed gated multi-modal neural networks which integrate wearable sensor input data selectively by using gate modules. For the experiments, sensory data were collected by using multiple wearable devices in restaurant situations. As an experimental result, we first show that the proposed model performs well in terms of prediction accuracy. Then, the possibility to construct a knowledge schema automatically by analyzing the activation patterns in the middle layer of our proposed model is explained.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.36-39
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• 2011

In this paper, performance of EMI interface and multi-channel wireless impedance sensor node is evaluated for SHM on bolted connection. To achieve the objective, following approaches are implemented. Firstly, an interface washer is designed to monitor loosened bolt through the variation in EMI of interface washer due to change in preload in bolt. Secondly, a multi-channel wireless impedance sensor node based on Imote2 platform is designed for automated and cost-efficient impedance-based SHM on bolted connections. Finally, performance of the multi-channel wireless impedance sensor node and the interface washer are experimentally validated for a lab-scale bolted connection model. A damage monitoring method using RMSD index of EMI signatures is utilized to examine the strength of each individual bolted connection.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.125-134
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• 2022

Recently, there are an increased the number of reports on the misuse or malicious use of an UAS. Thus, many researchers are studying on defense schemes for UAS by developing or improving C-UAS sensor technology. However, the wrong placement of sensors may lead to a defense failure since the proper placement of sensors is critical for UAS defense. In this study, a multi-object optimization model for C-UAS sensor placement in an air base is proposed. To address the issue, we define two objective functions: the intersection ratio of interested area and the minimum detection range and try to find the optimized placement of sensors that maximizes the two functions. C-UAS placement model is designed using a NSGA-II algorithm, and through experiments and analyses the possibility of its optimization is verified.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.248-254
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• 2024

Artificial intelligence techniques have improved fire-detection methods; however, false alarms still occur. Conventional methods detect fires using current sensors, which can lead to detection errors due to temporary environmental changes or noise. Thus, fire-detection methods must include a trend analysis of past information. We propose a deep-learning-based fire detection method using multi-sensor data and Kendall's tau. The proposed system used a BiLSTM model to predict fires using pre-processed multi-sensor data and extracted trend information. Kendall's tau indicates the trend of a time-series data as a score; therefore, it is easy to obtain a target pattern. The experimental results showed that the proposed system with trend values recorded an accuracy of 99.93% for BiLSTM and GRU models in a 20-tap moving average filter and 40% fire threshold. Thus, the proposed trend approach is more accurate than that of conventional approaches.

• The KIPS Transactions:PartC
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• v.17C no.5
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• pp.433-440
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• 2010

Energy efficiency, low latency and scalability for wireless sensor networks are important requirements, especially, the wireless sensor network consist of a large number of sensor nodes should be minimized energy consumption of each node to extend network lifetime with limited battery power. An efficient algorithm and energy management technology for minimizing the energy consumption at each sensor node is also required to improve transfer rate. Thus, this paper propose no-overlap multi-pass protocol provides for sensor data transmission in the wireless sensor network environment. The proposed scheme should minimize network overhead through reduced a sensor data translation use to searched multi-path and added the multi-path in routing table. Proposed routing protocol may minimize the energy consumption at each node, thus prolong the lifetime of the sensor network regardless of where the sink node is located outside or inside the received signal strength range. To verify propriety proposed scheme constructs sensor networks adapt to current model using the real data and evaluate consumption of total energy.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.587-592
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• 1994

To realize artificial device with sensing ability of the human skin, a mono-material tactile sensor with three sensing functions made of some elastic thin electro-conductive rubber sheet with eight latticed patch elements is proposed. This trial sensor provides the information of three kinds of model material characteristics such as thermal property, hardness property and the surface situation of materials by setting up three kinds of surface models as test materials. It can be finally expected to estimate unknown model materials by analyzing the data of the sensor.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.