• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.15-28
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• 2019

By applying the method of SAR interferometry to X-band synthetic aperture radar (SAR) image of COSMO-SkyMed, detailed motion patterns of five glaciers in the Parlung Zangbo River basin, Tibetan Plateau, in January 2010 have been derived. The results indicate that flow patterns are generally constrained by the valley geometry and terrain complexity. The maximum of $123.9ma^{-1}$ is observed on glacier No.1 and the minimum of $39.4ma^{-1}$ is found on glacier No.3. The mean values of five glaciers are between 22.9 and $98.2ma^{-1}$. Glaciers No.1, No.2, No.4 and No.5 exhibit high velocities in their upper sections with big slope and low velocities in the lower sections. A moraine lake accelerates the speed of mass exchange leading to a fast flow at the terminal of glacier No.3. These glaciers generally move along the direction of decreased elevation and present a macroscopic illustration of the motion from the northwest to the southeast. The accuracy of DEM and registration conditions of DEM-simulated terrain phases has certain effects on calculations of glacier flow direction and velocity. The error field is relatively fragmented in areas inconsistent with the main flow line of the glaciers, and the shape and uniformity of glacier are directly related to the continuous distribution of flow velocity errors.

• Journal of IKEEE
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• v.24 no.4
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• pp.1034-1038
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• 2020

In order to effectively process target recognition using radar, accurate signal information for the target is required. However, such a target signal is usually mixed with noise, and this part of the study is continuously carried out. Especially, image processing, target signal processing and target recognition for the target are examples. Since the field of target recognition is important from a military point of view, this paper carried out research on target recognition of aircraft using a tree-structured fuzzy neural networks. Fuzzy neural networks are learned by using reflected signal data for an aircraft to optimize the model, and then test data for the target are used for the optimized model to perform an experiment on target recognition. The effectiveness of the proposed method is verified by the simulation results.

• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.895-902
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• 2022

Building segmentation using satellite imagery such as EO (Electro-Optical) and SAR (Synthetic-Aperture Radar) images are widely used due to their various uses. EO images have the advantage of having color information, and they are noise-free. In contrast, SAR images can identify the physical characteristics and geometrical information that the EO image cannot capture. This paper proposes a learning framework for efficient building segmentation that consists of a teacher-student-based privileged knowledge distillation and deformable convolution block. The teacher network utilizes EO and SAR images simultaneously to produce richer features and provide them to the student network, while the student network only uses EO images. To do this, we present objective functions that consist of Kullback-Leibler divergence loss and knowledge distillation loss. Furthermore, we introduce deformable convolution to avoid pixel-level noise and efficiently capture hard samples such as small and thin buildings at the global level. Experimental result shows that our method outperforms other methods and efficiently captures complex samples such as a small or narrow building. Moreover, Since our method can be applied to various methods.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• Journal of Information Technology Services
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• v.22 no.2
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• pp.57-70
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• 2023

In order to establish an autonomous driving environment, it is necessary to study traffic safety and demand prediction by analyzing information generated from the transportation infrastructure beyond relying on sensors by the vehicle itself. In this paper, we propose a real-time traffic information generation method using sensor convergence technology of transportation infrastructure. The proposed method uses sensors such as cameras and radars installed in the transportation infrastructure to generate information such as crosswalk pedestrian presence or absence, crosswalk pause judgment, distance to stop line, queue, head distance, and car distance according to each characteristic. create information An experiment was conducted by comparing the proposed method with the drone measurement result by establishing a demonstration environment. As a result of the experiment, it was confirmed that it was possible to recognize pedestrians at crosswalks and the judgment of a pause in front of a crosswalk, and most data such as distance to the stop line and queues showed more than 95% accuracy, so it was judged to be usable.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.147-155
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• 2024

Based on the recently developed deep learning technology, many studies have been conducted on deep learning networks that simultaneously detect and classify targets of interest in synthetic aperture radar(SAR) images. Although numerous research results have been derived mainly with the open SAR ship datasets, there is a lack of work carried out on the deep learning network aimed at detecting and classifying SAR ground targets and trained with the synthetic dataset generated from electromagnetic scattering simulations. In this respect, this paper presents the deep learning network trained with the synthetic dataset and applies it to detecting and classifying real SAR ground targets. With experiment results, this paper also analyzes the network performance according to the composition ratio between the real measured data and the synthetic data involved in network training. Finally, the summary and limitations are discussed to give information on the future research direction.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.6
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• pp.573-582
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• 2010

In this paper, X-band transceiver for high resolution wideband SAR systems is designed and fabricated. Also as a technique for enhancing the performance, error compensation algorithm is presented. The transceiver for SAR system is composed of transmitter, receiver, switch matrix and frequency generator. The receiver especially has 2 channel mono-pulse structure for ground moving target indication. The transceiver is able to provide the deramping signal for high resolution mode and select the receive bandwidth for receiving according to the operation mode. The transceiver had over 300 MHz bandwidth in X-band and 13.3 dBm output power which is appropriate to drive the T/R module. The receiver gain and noise figure was 39 dB and 3.96 dB respectively. The receive dynamic range was 30 dB and amplitude imbalance and phase imbalance of I/Q channel was ${\pm}$0.38 dBm and ${\pm}$3.47 degree respectively. The transceiver meets the required electrical performances through the individual tests. This paper shows the pulse error term depending on SAR performance was analyzed and range IRF was enhanced by applying the compensation technique.

• Journal of Distribution Science
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• v.10 no.10
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• pp.17-24
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• 2012

Purpose - This study aimed to (a) investigate the effects of emotional intelligence on customer orientation, (b) examine the impact of customer orientation on customer relationship management (CRM) performance (including CRM-related variables such as 'relationship commitment,' 'image of corporation,' and 'customer loyalty'), and (c) identify the conceptual framework of emotional intelligence. Research design, data, and methodology - The data were collected using a questionnaire given to a sample of employees of luxury hotels in the metropolitan area. To test the hypotheses, AMOS were conducted for the 271 respondents of the sample using the SPSS Win 17.0 software. The concept of emotional intelligence (EI) has been on the radar of many leaders and managers over the past few decades. Emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thoughts, and interactions with others. Emotional intelligence consists of four factors: understanding the self's emotion, understanding other people's emotions, emotion utilization, and emotion control. Understanding the self's emotion means to understand of my own emotions. Understanding other people's emotions is to understand of the emotions of the people around me and to know how my friends feel based on their behavior. The concept of emotion utilization means to set goals for myself and then try to achieve them, encouraging myself to do my best. The concept of emotion control means I can control my temper, handle difficult situations rationally, and calm down quickly when I am very angry. Results - As a result of the analysis, three factors (understanding the self's emotion, understanding of other people's emotions, and emotion utilization) were shown to have a significant effect on customer orientation. Emotion control had an insignificant effect on customer orientation. Only emotion control makes it difficult to solve customers' problems because it is a passive behavior. In order to solve the customers' problems, hotel employees have to show a positive attitude. Second, customer orientation had a significant effect on customer relationship management performance (customer relationship commitment, corporate image, and customer loyalty). In other words, customer orientation increases commitment to customer relationships. For example, employees who have a customer-orientated perspective provide good service to their customers, while employees who don't have a customer-orientated perspective can't satisfy their customers. Customer orientation can also generate a good image among customers, because they evaluate the image of a hotel through the behavior of hotel employees. So it is very important for employees to show excellent customer orientation. Conclusions - It is very important for hotel CEOs to manage their employees' emotional intelligence. In order to increase their employees' emotional intelligence abilities, CEOs have to manage the overall corporate culture and reward programs to achieve what they want. This is because the system can lead to a customer-orientated mind-set and CRM performance among employees. As a result, the hotel CEO has to pay attention to the emotional intelligence of employees to achieve strong CRM performance. The sentence as originally written was a bit unclear. If this edit does not retain your intended meaning please consider: "Only emotion control does not have a significant impact on customer orientation, and therefore on the ability of an employee to solve customer problems, because it is a passive behavior." Please use the version of the sentence that best captures your original meaning.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.575-585
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• 2011

The effect of wavelet pair choice in the compression of the satellite images is studied. There is a trade-off between compression rate and perception quality. The encoding ratio is used to express the compression rate, and Peak Signal-to-Noise Ratio (PSNR) is also used for the perceptional performance. The PSNR and the encoding ratio are not matched well for the images with various wavelet pairs, but the tendency is remarkable. It is hard to find the pattern of PSNR for sampled images. On the other hand, there is a pattern of the variation range of the encoding ratio for each image. The satellite images have larger values of the encoding ratio than those of nature images (close range images). Depending on the wavelet pairs, the PSNR and the encoding ratio vary as much as 13.2 to 21.6% and 16.8 to 45.5%, respectively for each image. For Synthetic Aperture Radar (SAR) images the encoding ratio varies from 16 to 20% while for the nature images it varies more than 40% depending on the choice of wavelet pairs. The choice of wavelet for the compression affects the nature images more than the satellite images. With the indices such as the PSNR and the encoding ratio, the satellite images are less sensitive to the choice of wavelet pairs. A new index, energy concentration ratio (ECR) is proposed to investigate the effect of wavelet choice on the satellite image compression. It also shows that the satellite images are less sensitive than the nature images. Nevertheless, the effect of wavelet choice on the satellite image compression varies at least 10% for all three kinds of indices. However, the important of choice of wavelet pairs cannot be ignored.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.83-107
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• 2014

This study presents spatial characteristics of cloud using satellite image in the extreme heavy snowfall of the Yeongdong region. 3 extreme heavy snowfall events in the Yeongdong region during the recent 12 years (2001 ~ 2012) are selected for which the fresh snow cover exceed 50 cm/day. Spatial characteristics (minimum brightness temperature; Tmin, cloud size, center of cloud-cell) of cloud are analyzed by tracking main cloud-cell related with these events. These characteristics are compared with radar precipitation in the Yeongdong region to investigate relationship between cloud and precipitation. The results are summarized as follows, selected extreme heavy snowfall events are associated with the isolated, well-developed, and small-scale convective cloud which is developing over the Yeongdong region or moving from over East Korea Bay to the Yeongdong region. During the period of main precipitation, cloud-cell Tmin is low ($-40{\sim}-50^{\circ}C$) and cloud area is small (17,000 ~ 40,000 $km^2$). Precipitation area (${\geq}$ 0.5 mm/hr) from radar also shows small and isolated shape (4,000 ~ 8,000 $km^2$). The locations of the cloud and precipitation are similar, but in there centers are located closely to the coast of the Yeongdong region. In all events the extreme heavy snowfall occur in the period a developed cloud-cell was moving into the coastal waters of the Yeongdong. However, it was found that developing stage of cloud and precipitation are not well matched each other in one of 3 events. Water vapor image shows that cloud-cell is developed on the northern edge of the dry(dark) region. Therefore, at the result analyzed from cloud and precipitation, selected extreme heavy snowfall events are associated with small-scale secondary cyclone or vortex, not explosive polar low. Detection and tracking small-scale cloud-cell in the real-time forecasting of the Yeongdong extreme heavy snowfall is important.