• Korean Journal of Remote Sensing
• /
• v.20 no.4
• /
• pp.235-244
• /
• 2004

This study focuses on the assessment for proposed algorithm to discriminate cloudy pixels from snowy pixels through use of visible, near infrared, and short wave infrared channel data in VEGETATION-1 sensor embarked on SPOT-4 satellite. Traditional threshold algorithms for cloud and snow masks did not show very good accuracy. Instead of these independent masking procedures, K-Means clustering scheme is employed for cloud/snow discrimination in this study. The pixels used in clustering were selected through an integration of two threshold algorithms, which group ensemble the snow and cloud pixels. This may give a opportunity to simplify the clustering procedure and to improve the accuracy as compared with full image clustering. This paper also compared the results with threshold methods of snow cover and clouds, and assesses discrimination capability in VEGETATION channels. The quality of the cloud and snow mask even more improved when present algorithm is implemented. The discrimination errors were considerably reduced by 19.4% and 9.7% for cloud mask and snow mask as compared with traditional methods, respectively.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.2
• /
• pp.73-78
• /
• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.613-626
• /
• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.181-193
• /
• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2004.03a
• /
• pp.802-806
• /
• 2004

For the protection of the local air quality and the global atmosphere, the emissions of trace species including nitric oxides (NO and NO$_2$) from gas turbines are regulated by local governments and by the International Civil Aviation Organization. In-situ measurements of such species are needed not only for the development of advanced low-emission combustion concepts but also for providing emissions data required for the sound assessment of the effects of the emissions on environment. We have been developing a laser absorption system that has a capability of simultaneous determination of NO and NO$_2$concentrations in the exhaust jets from aero gas turbines. A diode laser operating near 1.8 micrometer is used for the detection of NO while a separated visible tunable diode laser operating near 676 nanometers is used for NO$_2$. The sensitivities at elevated temperature conditions were determined for simulated gas mixtures heated up to 500K in a heated cell of a straight 0.5 m optical path. Sensitivity limits estimated as were 30 ppmv-m and 3.7 ppmv-m for NO and NO$_2$, respectively, at a typical exhaust gas temperature of 800K. Experiments using the simulated exhaust flows have proven that $CO_2$ and $H_2O$ vapor - both major combustion products - do not show any interference in the NO or NO$_2$ measurements. The measurement system has been applied to the NO/NO$_2$ measurements in NO and NO$_2$ doped real combustion gas jets issuing from a rectangular nozzle having 0.4 m optical path. The lower detection limits of the system were considerably decreased by using a multipass optical cell. A pair of off-axis parabola mirrors successfully suppressed the beam steering in the combustion gas jets by centralizing the fluctuating beam in sensor area of the detectors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.62-65
• /
• 2022

NCS(Naval Combat System) is a system that maximizes the combat effectiveness for the naval ships by providing track detection, tracking, thereat analysis, engagement, hit assessment and many other capabilities using ship integrated heterogeneous sensor and weapon systems. In order to achieve the purpose of the NCS, every crew is require to be proficient in the operation of NCS. In accordance with the goal, NCS provides a onboard training function, and the crew conducts system operation proficiency and teamwork training on the ship. Training instructors for control training should have a high standard of training environment control and monitoring capabilities, which need to be studied. This paper studies a 3D display method for visualizing the training situation of training instructors.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.2A
• /
• pp.161-168
• /
• 2010

The bonded tendon was adopted to the reactor building of some operating nuclear power plants in Korea and the assessment of the effective prestress force on the bonded tendon is being issued as an important pending problem for continuous operation beyond their design life. The sensitivity analysis of various parameters was carried out to evaluate the effective prestress force using the system identification technique and the optimal parameters were determined for SI technique in this study. The 1/5 scaled post-tensioned concrete beams with the bonded tendon type were manufactured and in order to investigate the relationship of the natural frequency and the displacement to the effective prestress force, impact test, SIMO sine sweep test and bending test using the optical fiber sensor and the compact displacement transducer were carried out. As a result of tests, both the natural frequency and the displacement show the good relationship with the effective prestress force and both parameters are available for the SI technique to estimate the effective prestress force.

• Fashion & Textile Research Journal
• /
• v.25 no.6
• /
• pp.673-689
• /
• 2023

To realize the traceability of sustainable textile products, this study presents a low-carbon process through energy savings in the textile material manufacturing process. Traceability is becoming an important element of Life Cycle Assessment (LCA), which confirms the eco-friendliness of textile products as well as supply chain information. Textile products with complex manufacturing processes require traceability of each step of the process to calculate carbon emissions and power usage. Additionally, an understanding of the characteristics of the product planning-manufacturing-distribution process and an overall understanding of carbon emissions sources are required. Energy use in the textile material manufacturing stage produces the largest amount of carbon dioxide, and the amount of carbon emitted from processes such as dyeing, weaving and knitting can be calculated. Energy saving methods include efficiency improvement and energy recycling, and carbon dioxide emissions can be reduced through waste heat recovery, sensor-based smart systems, and replacement of old facilities. In the dyeing process, which uses a considerable amount of heat energy, LNG, steam can be saved by using "heat exchangers," "condensate management traps," and "tenter exhaust fan controllers." In weaving and knitting processes, which use a considerable amount of electrical energy, about 10- 20% of energy can be saved by using old compressors and motors.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.10
• /
• pp.2627-2642
• /
• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.837-847
• /
• 2023

In this study, we explored a method for assessing the extent of damage caused by chemical substances at an accident site through the use of a vegetation index. Data collection involved the deployment of two different drone types, and the damaged area was determined using photogrammetry technology from the 3D point cloud data. To create a vegetation index image, we utilized spectral band data from a multi-spectral sensor to generate an orthoimage. Subsequently, we conducted statistical analyses of the accident site with respect to the damaged area using a predefined threshold value. The Kappa values for the vegetation index, based on the near-infrared band and the green band, were found to be 0.79 and 0.76, respectively. These results suggest that the vegetation index-based approach for analyzing damage areas can be effectively applied in investigations of chemical accidents.