• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2427-2432
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• 2014

Color-$X^2$ is used as a method for scene change detection. It extracts a violent scene in an elevator and then could be used for real-time surveillance of criminal acts. The scene could be also used to secure after-discovered evidences and to prove analysis processes. Video Forensic is defined as a research on various methods to efficiently analyze evidences upon crime-related visual images in the field of digital forensic. The method to use differences of color-histogram detects the difference values of histogram for RGB color from two frames respectively. Our paper uses Color-$X^2$ histogram that is composed of merits of color histogram and ones of $X^2$ histogram, in order to efficiently extract violent scenes in elevator. Also, we use a threshold so as to find out key frame, by use of existing Color-$X^2$ histogram. To increase the probability that discerns whether a real violent scene or not, we take advantage of statistical judgments with 20 sample visual images.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.66-76
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• 2009

The motion estimation in video encoding system occupies the biggest part. So, we require the motion estimator with efficient structure for real-time operation. And for motion estimator's implementation, it is desired to design hardware module of an exclusive use that perform the encoding process at high speed. This paper proposes motion estimation detection block(MED), 41 SADs(Sum of Absolute Difference) calculation block, minimum SAD calculation and motion vector generation block based on parallel processing. The parallel processing can reduce effectively the amount of the operation. The minimum SAD calculation and MED block uses the pre-computation technique for reducing switching activity of the input signal. It results in high-speed operation. The MED and 41 SADs calculation blocks are composed of adder tree which causes the problem of critical path. So, the structure of adder tree has changed the most commonly used ripple carry adder(RCA) with carry skip adder(CSA). It enables adder tree to operate at high speed. In addition, as we enabled to easily control key variables such as control signal of search range from the outside, the efficiency of hardware structure increased. Simulation and FPGA verification results show that the delay of MED block generating the critical path at the motion estimator is reduced about 19.89% than the conventional strukcture.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.141-148
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• 2021

In some intersections or busy traffic roads, there are more pedestrians in a specific period of time, and there are many traffic accidents caused by road congestion. Especially at the intersection where there are schools nearby, it is particularly important to protect the traffic safety of students in busy hours. In the past, when designing traffic lights, the safety of pedestrians was seldom taken into account, and the identification of motor vehicles and traffic optimization were mostly studied. How to keep the road smooth as far as possible under the premise of ensuring the safety of pedestrians, especially students, will be the key research direction of this paper. This paper will focus on person, motorcycle, bicycle, car and bus recognition research. Through investigation and comparison, this paper proposes to use YOLO v4 network to identify the location and quantity of objects. YOLO v4 has the characteristics of strong ability of small target recognition, high precision and fast processing speed, and sets the data acquisition object to train and test the image set. Using the statistics of the accuracy rate, error rate and omission rate of the target in the video, the network trained in this paper can accurately and effectively identify persons, motorcycles, bicycles, cars and buses in the moving images.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.171-182
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• 2022

InSAR (Interferometry SAR) technique is a technique that uses complex data to obtain phase difference information from two or more SAR image data, and enables high-resolution image extraction, surface change detection, elevation measurement, and glacial change observation. In many countries, research on the InSAR technique is being conducted in various fields of study such as volcanic activity detection, glacier observation in Antarctica, and ground subsidence analysis. In this study, a case of large ground settlement due to groundwater level drawdown during tunnelling was introduced, and ground settlement analyses using InSAR technique and numerical analysis method were compared. The maximum settlement and influence radius estimated by the InSAR technique and numerical method were found to be quite similar, which confirms the reliability of the InSAR technique. Through this case study, it was found that the InSAR technique reliable to use for estimating ground settlement and can be used as a key technology to identify the long-term ground settlement history in the absence of measurement data.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.194-210
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• 2023

LiDAR plays a key role in autonomous vehicles, where it is used to detect the environment in place of the driver's eyes, and its role is expanding. In recent years, there has been a growing need to test the performance of LiDARs installed in autonomous vehicles. Many LiDAR performance tests have been conducted in simulated and indoor(lab) environments, but the number of tests in outdoor(field) and real-world road environments has been minimal. In this study, we compared LiDAR performance under the same conditions lab and field to determine the relationship between lab and field tests and to establish the characteristics and roles of each test environment. The experimental results showed that LiDAR detection performance varies depending on the lighting environment (direct sunlight, led) and the detected object. In particular, the effect of decreasing intensity due to increasing distance and rainfall is greater outdoors, suggesting that both lab and field experiments are necessary when testing LiDAR detection performance on objects. The results of this study are expected to be useful for organizations conducting research on the use of LiDAR sensors and facilities for LiDAR sensors.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.55-66
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• 2023

In recent years, naval vessels have been developed to fulfill a variety of missions by being equipped with various cutting-edge equipment and ICT technologies. One of the main missions of Korean naval vessels is anti-ballistic missile warfare to defend key units and areas against the growing threat of ballistic missiles. Because the process of detection and interception is too complex and the cost of failure is much high, a lot of preparation is required to effectively conduct anti-ballistic missile warfare. This paper describes the development of a simulation model of anti-ballistic missile warfare with combat systems and equipment to be installed on future naval vessels. In particular, the DEVS formalism providing a modular and hierarchical modeling manner was applied to the simulation model, which can be utilized to efficiently represent various anti-ballistic missile warfare situations. In the simulation results presented, experiments were conducted to analyze the effectiveness of the model for effective detection resource management in anti-ballistic missile warfare. This study is expected to be utilized as a variety of analysis tools necessary to determine the optimal deployment and configuration of combat resources and operational tactics required for effective anti-ballistic missile warfare of ships in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.160-174
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• 2023

LiDAR plays a key role in autonomous vehicles, and to improve its visibility, it is necessary to improve its performance and the detection objects. Accordingly, this study proposes a shape for traffic safety signs that is advantageous for self-driving vehicles to recognize. Improvement plans are also proposed using a shape-recognition algorithm based on point cloud data collected through LiDAR sensors. For the experiment, a DBSCAN-based road-sign recognition and classification algorithm, which is commonly used in point cloud research, was developed, and a 32ch LiDAR was used in an actual road environment to conduct recognition performance tests for 5 types of road signs. As a result of the study, it was possible to detect a smaller number of point clouds with a regular triangle or rectangular shape that has vertical asymmetry than a square or circle. The results showed a high classification accuracy of 83% or more. In addition, when the size of the square mark was enlarged by 1.5 times, it was possible to classify it as a square despite an increase in the measurement distance. These results are expected to be used to improve dedicated roads and traffic safety facilities for sensors in the future autonomous driving era and to develop new facilities.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3913-3923
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• 2023

Todays, medium energy resolution detectors are preferably used in radioisotope identification devices(RID) in nuclear and radioactive material categorization. However, there is still a need to develop or enhance « automated identifiers » for the useful RID algorithms. To decide whether any material is SNM or NORM, a key parameter is the better energy resolution of the detector. Although masking, shielding and gain shift/stabilization and other affecting parameters on site are also important for successful operations, the suitability of the RID algorithm is also a critical point to enhance the identification reliability while extracting the features from the spectral analysis. In this study, a RID algorithm based on Bayesian statistical method has been modified for medium energy resolution detectors and applied to the uranium gamma-ray spectra taken by a LaBr3:Ce detector. The present Bayesian RID algorithm covers up to 2000 keV energy range. It uses the peak centroids, the peak areas from the measured gamma-ray spectra. The extraction features are derived from the peak-based Bayesian classifiers to estimate a posterior probability for each isotope in the ANSI library. The program operations were tested under a MATLAB platform. The present peak based Bayesian RID algorithm was validated by using single isotopes(²⁴¹Am, ⁵⁷Co, ¹³⁷Cs, ⁵⁴Mn, ⁶⁰Co), and then applied to five standard nuclear materials(0.32-4.51% at.²³⁵U), as well as natural U- and Th-ores. The ID performance of the RID algorithm was quantified in terms of F-score for each isotope. The posterior probability is calculated to be 54.5-74.4% for ²³⁸U and 4.7-10.5% for ²³⁵U in EC-NRM171 uranium materials. For the case of the more complex gamma-ray spectra from CRMs, the total scoring (ST) method was preferred for its ID performance evaluation. It was shown that the present peak based Bayesian RID algorithm can be applied to identify ²³⁵U and ²³⁸U isotopes in LEU or natural U-Th samples if a medium energy resolution detector is was in the measurements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.97-104
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• 2024

As indoor air pollution has emerged as a social issue since the COVID-19 pandemic, pollution management in large-scale facilities has been recognized as an important task. For this purpose, this study proposes real-time pollution level detection using sensors and efficient control path setting using Dijkstra algorithm as key technologies. In addition, by introducing outlier determination algorithm and priority algorithm, we propose ways to increase the reliability of the data and enable efficient control work. The outlier determination algorithm describes the process of identifying and processing outliers based on sensor data in an environmental monitoring system. It describes in detail the process of averaging the recent 10 sensor data, calculating the Z-score to detect outliers, and removing and replacing the data determined to be outliers. The priority algorithm describes the process of establishing an efficient control path in consideration of the pollution level of each region. It suggests how to select the most polluted areas first and use them as a starting point to set the control path. In addition, it introduces an iterative process of detecting and responding to the pollution level in real time, which allows the system to be continuously optimized and to respond to environmental pollution. Through this, it is expected to increase the reliability and efficiency of the environmental monitoring system through outlier judgment algorithms and priority algorithms, thereby quickly identifying and responding to pollution situations.