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기존의 많은 돌발상황 자동감지 알고리즘은 복잡한 구조와 계산 과정, 수많은 매개변수, 그리고 필터링/평활화 같은 선 작업 때문에 지속적인 유지관리가 사실상 중단된 상태이고 오보율 또한 높아 많은 교통관리센터로부터 기피 대상이 되고 있는 등 돌발상황감지의 주력 수단으로서 자동 알고리즘의 지위가 위태해진 현실은 매우 우려할만하다. 본 연구에서는 상대 점유율과 속도 항을 활용하여 적체 교통량이라는 신 개념을 도입, 구조가 아주 간단하면서도 검 측 원시자료의 보정이 거의 필요 없는 DiFI(Displaced Flow Index) 기반의 돌발상황 자동감지알고리즘을 개발하였다. DiFI 알고리즘의 성능평가는 2003년도 내부순환로 검지기자료를 활용하여 검증을 수행하였으며, 2011년도 경부고속 도로 검지기 자료를 수집·정리하여 이식성 검사를 이행하였다. 성능평가는 검지율, 오보율, 평균검지시간, 기타 CR, CI, PI를 사용하였는데 100%의 검지율과 2.99%의 낮은 오보율, 1분을 약간 초과하는 평균검지시간을 보였다. 이는 SAO 는 물론 국내 현장에 가장 많이 접목된 APID 및 DELOS 등과 비교해서도 모든 면에서 우월한 성능을 보이는 것이었다.

Many traffic centers are highly hesitant in employing existing Automatic Incident Detection Algorithms due to high false alarm rate, low detection rate, and enormous effort taken in maintaining algorithm parameters, together with complex algorithm structure and filtering/smoothing process. Concerns grow over the situation particularly in Freeway Incident Management Area This study proposes a new algorithm and introduces a novel concept, the Displaced Flow Index (DiFI) which is similar to a product of relative speed and relative occupancy for every execution period. The algorithm structure is very simple, also easy to understand with minimum parameters, and could use raw data without any additional pre-processing. To evaluate the performance of the DiFI algorithm, validation test on the algorithm has been conducted using detector data taken from Naebu Expressway in Seoul and following transferability tests with Gyeongbu Expressway detector data. Performance test has utilized many indices such as DR, FAR, MTTD (Mean Time To Detect), CR (Classification Rate), CI (Composite Index) and PI (Performance Index). It was found that the DR is up to 100%, the MTTD is a little over 1.0 minutes, and the FAR is as low as 2.99%. This newly designed algorithm seems promising and outperformed SAO and most popular AIDAs such as APID and DELOS, and showed the best performance in every category.

검지기 자료의 품질은 동일한 교통 조건이라도 검지기의 종류, 보정 및 유지 보수의 노력, 도로의 기하구조 등에 의해 내재적으로 10% 이상 변수 값 차이를 보일 수 있다. 이러한 내재적인 변수 값 차이로 인해 성능이 검증된 돌발상황 자동감지알고리즘을 사용하더라도 검지율(DR)이 낮아지고 오보율(FAR)이 높아지는 문제가 야기될 수 있다. 본 연구는 교통특성 변수의 간단한 산술계산기법(SAO)를 활용하여 새로운 AIDA를 개발하는데 목적이 있다. SAO는 내재적으로 존재하는 변수 값 차이를 보정하여 AIDA의 성능 저하를 극복하기 위해 개발되었다. AIDA 모형의 개발 및 검증을 위해 내부순환도로와 경부고속도로에서 수집된 30초 단위의 점유율을 사용하였다. SAO 기반 알고리즘 분석 결과 100% 돌발상황을 검지하는 높은 검지율을 보였고, 검지시간(MTTD)은 1분 이내로 나타났다. 오보율의 경우 본 연구에서 개발된 SAO 기반 AIDA의 오보율이 기개발된 AIDA의 오보율보다 3.5%~31.0% 감소하는 것을 확인하였다. SAO 기반의 알고리즘은 돌발상황의 지속성 분석에서도 탁월한 능력을 보였다.

The data quality and measurements along consecutive detector stations can vary much even in the same traffic conditions due to variety in detector types, calibration and maintenance effort, field operation periods, minor geometric changes of roads and so on. These faulty situations often create 10% or more of inherent difference in important traffic measurements between two stations even under stable low flow condition. Low detection rates(DR) and high false alarm rates(FAR) therefore sets in among many popular Automatic Incident Detection Algorithms(AIDA). This research is two-folded and aims mainly to develop a new AIDA for uninterrupted flow. For this purpose, a technique which utilizes a Simple Arithmetic Operation(SAO) of traffic variables is introduced. This SAO technique is designed to address the inherent discrepancy of detector data observed successive stations, and to overcome the degradation of AIDA performance. It was found that this new algorithm improves DR as much as 95 percent and above. And mean time to detection(MTTD) is found to be 1 minutes or less. When it comes to FAR, this new approach compared to existing AIDAs reduces FAR up to 31.0 percent. And capability in persistency check of on-going incidents was found excellent as well.

프로그래밍 언어 교육은 컴퓨터의 기본적인 동작 원리를 학습하는데 도움을 주고, 논리적 사고력과 같은 여 러 가지 문제 해결력을 길러주기 때문에 2015년도부터 초, 중, 고등학교 교육과정에 SW를 포함시켰다. 프로그 래밍 언어는 다양하지만 그 중에서 C언어는 구성의 단순성, 하드웨어의 효율성, 오랜 역사가 있다는 장점으로 인해 다양한 분야에서 사용 중이다. 그러나 보안에 관련된 오류는 C언어를 사용할 때 매우 치명적이다. C언어 의 보안 오류는 운영체제에서 문제를 발생시키고, 걷잡을 수 없이 확산되어 큰 피해를 입힐 수 있다. 따라서 본 논문에서는 C언어의 메모리 취약성 중 버퍼 오버플로우, 포인터 오류, 포맷 스트링, 정수 오버플로우 등 주요 네 가지 에러를 선정하고, 이들의 특징을 분석하여 오류 검출 및 수정 방법을 제시하는 ‘메모리 취약성 오류 검 출 및 자동 수정’ 알고리즘을 제안한다. 제안한 알고리즘을 통해 C언어 메모리 취약성의 심각한 오류와 피해가 줄어드는 것에 많은 도움을 줄 수 있을 것이다.

Since 2015, programming has been included in school curricula to enhance computer literacy and problem-solving skills. C language, widely used for its simplicity, efficiency, and long history, poses significant security risks, particularly in memory vulnerabilities like buffer overflow, pointer errors, format strings, and integer overflow. These vulnerabilities can cause severe system issues and widespread damage. This paper proposes an "Error Detection and Automatic Correction of Memory Vulnerabilities (EDAC)" algorithm to detect and correct these errors, aiming to reduce the impact of C language memory vulnerabilities.

Inter-granular Bright Points (igBPs) are small-scale objects in the Solar photosphere which can be seen within dark inter-granular lanes. We present a new algorithm to automatically detect and extract igBPs. Laplacian and Morphological Dilation (LMD) technique is employed by the algorithm. It involves three basic processing steps: (1) obtaining candidate "seed" regions by Laplacian; (2) determining the boundary and size of igBPs by morphological dilation; (3) discarding brighter granules by a probability criterion. For validating our algorithm, we used the observed samples of the Dutch Open Telescope (DOT), collected on April 12, 2007. They contain 180 high-resolution images, and each has a $85{\times}68\;arcsec^2$ field of view (FOV). Two important results are obtained: first, the identified rate of igBPs reaches 95% and is higher than previous results; second, the diameter distribution is $220{\pm}25km$, which is fully consistent with previously published data. We conclude that the presented algorithm can detect and extract igBPs automatically and effectively.

Detecting and quantifying cracks in bituminous (asphalt) road surfaces plays a crucial role in maintaining road infrastructure integrity and enabling cost-effective maintenance strategies. However, traditional manual inspections are laborious, time-intensive, and susceptible to inconsistencies due to factors like human fatigue, varying expertise levels, and subjective assessments. To address these challenges, this research proposes CrackNet, an innovative deep learning framework that harnesses state-of-the-art computer vision and object detection techniques for accurate and computationally efficient automated crack detection in bituminous road imagery. CrackNet introduces a novel hybrid neural network architecture that seamlessly integrates a cutting-edge Vision Transformer backbone with multi-scale convolutional feature fusion modules. The Vision Transformer component excels at capturing long-range structural dependencies and global contextual information, while the multi-scale fusion modules adeptly combine fine-grained crack details across various spatial resolutions. This unique design enables CrackNet to holistically model intricate crack topologies while preserving localized characteristics and intricate details. To further bolster robustness and generalization capabilities across diverse real-world scenarios, CrackNet incorporates self-supervised pre-training techniques that leverage unlabeled data and unsupervised pretext tasks. These strategies allow CrackNet to learn rich visual representations tailored specifically for crack detection. Additionally, an extensive data augmentation pipeline is employed, encompassing geometric, photometric, and adversarial transformations, to enhance model invariance to varying imaging conditions and environmental factors. The accuracy achieved by the newly proposed approach surpasses that of current state-of-the-art methodologies, reaching an impressive 97.8%.

The delay of cardiac depolarization wave detection in the conventional pacemakers or AICD (automatic implantable cardioverter/ defibrillator, or ICD) has been overlooked. However, it is known that the delay may cause hemodynamic problems and may prevent the proper operation of a new automatic feature, automatic capture verification, that is to be appeared in the near-future devices. In order to reduce the effects of the delay, a delay prevention algorithm was developed and tested by applying three human electrograms. The algorithm set the sensing threshold just above the measured noise level to reduce the detection delay. It is found that the low threshold was able to reduce the delay by 20msec(average) In most cases. The implementation results showed reliability and efficacy of the algorithm, and the algorithm could be applicable to the existing hardware and software of the conventional pacemakers and AICD without any significant modifications.

Building detection is a fundamental and challenging task in satellite image analysis and has significant importance in the wide range of applications such as creation and update of maps and GIS database, urban monitoring, and planning application. This paper presents an approach for building detection in high resolution remotely sensed images. The proposed approach firstly segmented the pan-sharpened image by Automatic Histogram based Fuzzy C-Means (AHFCM) algorithm. Secondly the vegetation and shadow regions are eliminated by using Normalized Difference Vegetation Index (NDVI) and ratio map in HIS model respectively. Finally the buildings and non building regions are classified by using Principal Component Analysis (PCA) and outcomes are further refined by morphological operations.

In this paper, we proposed automatic extraction of brain tumor using morphological operation and statistical tumors size in MR images. Neurosurgery have used gamma-knife therapy by MR images. However, the gamma-knife plan systems needs the brain tumor regions, because gamma-ray should intensively radiate to the brain tumor except for normal cells. Therefore, gamma-knife plan systems spend too much time on designating the tumor regions. In order to reduce the time of designation of tumors, we progress the automatical extraction of tumors using proposed method. The proposed method consist of two steps. First, the information of skull at MRI slices remove using statistical tumors size. Second, the ROI is extracted by tumor feature and average of tumors size. The detection of tumor is progressed using proposed and threshold method. Moreover, in order to compare the effeminacy of proposed method, we compared snap-shot and results of proposed method.

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

최근 우리나라의 산림은 지속적인 산림재해로 인해 피해가 누적되고 있어 산림을 관리하기 위한 모니터링 기술이 조명받고 있으며, 산림재해 피해대상지의 규모가 큰 지형 특성으로 인해 드론, 인공지능, 빅데이터 등을 활용한 기술들이 연구되고 있다. 본 연구에서는 산림재해의 병해충을 모니터링하기 위해 딥러닝과 드론을 활용하여 산림 병해충 피해 의심목을 자동으로 탐지하는 산림 병해충 자동탐지 알고리즘 개발을 위한 표준 데이터 세트를 구축하였다. 객체검출 알고리즘으로서 YOLO 알고리즘을 활용한 실험결과에서는 YOLOv4-P7 모델이 재현율 69.69%와 정밀도 69.15%로 가장 높게 나타났으며, 이미지 사이즈가 큰 정사영상인 검출대상임을 고려할 때 산림병해충 피해의심목 자동탐지 알고리즘으로 YOLOv4-P7이 적합함을 확인하였다.

Recently, the forests in Korea have accumulated damage due to continuous forest disasters, and the need for technologies to monitor forest managements is being issued. The size of the affected area is large terrain, technologies using drones, artificial intelligence, and big data are being studied. In this study, a standard dataset were conducted to develop an algorithm that automatically detects suspicious trees damaged by forest pests using deep learning and drones. Experiments using the YOLO model among object detection algorithm models, the YOLOv4-P7 model showed the highest recall rate of 69.69% and precision of 69.15%. It was confirmed that YOLOv4-P7 should be used as an automatic detection algorithm model for trees suspected of being damaged by forest pests, considering the detection target is an ortho-image with a large image size.

An automatic multi-threshold algorithm for segmentation of 2D ultrasound images based on average filtering and the characteristics of speckle noise in 2D ultrasound image is proposed. To do this, we investigate the histogram of difference between $7{\times}7$ averaging histogram and $3{\times}3$ averaging histogram. And, we find zero crossing points in the positive portion of the differenced histogram and select middle points of the zero crossing points. We assign these selected points to characteristic points. The thresholds are the center of two characteristic points. Then we segment 2D ultrasound image by using these thresholds and extract edges from applying edge operator to optimal segmented image. Experimental results show that the segmented regions are devided accurately around the homogeneous region.

In this paper, we propose an automatic earthquake P-wave detection algorithm based on the variations of the impact momentum derived from the seismic acceleration signals. The amount of change in the impact momentum induced by the acceleration refers to the influence of buildings or facilities on the earthquake, The proposed algorithm can effectively detect the seismic P-wave by simultaneously considering the amplitude and the frequency change of the seismic wave when the earthquake occurs. Computer simulations using the observed seismic signals were performed to evaluate the validity of the induced impact momentum variation and the superiority of the proposed algorithm.

Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascular system status. In this study, we developed a capacitive ECG measurement system that can be included within a cloth mattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However, it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high level of noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithm that can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated by determining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Polysomnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%, and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% for our capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providing useful ECG information, even under noisy signal conditions.

건물에 출입하는 인원을 자동으로 검출하는 기술은 화재 등 재난 발생 시 요구조자의 피난을 위해 필요하다. 본 논문은 이를 위한 방안으로 출입구에 설치된 열화상 카메라를 이용하여 출입 인원의 볼륨 영역을 실시간 자동 검출하는 방안을 제안한다. 이와 같은 검출 시스템의 첫 단계는 이미지 내 정확한 인원을 예측하는 일이며 본 논문에서는 YoloV5 딥러닝 모듈을 적용하였다. 다음 단계는 본 논문에서 제안하는 열화상 물체의 이동 방향 분석 알고리즘이다. 이 알고리즘은 연속적으로 입력되는 열화상 볼륨 이미지의 순서를 분석하여 들어오는 인원과 나가는 인원을 구분하여 이동을 카운팅하였다. 제안하는 알고리즘은 이미지에서 재실자가 이동하는 주 영역을 4 개의 부분 영역으로 나누고 각 부분 영역마다 물체 볼륨 모양과 분포 변화를 추적하는 방식으로 구성하였다. 제안하는 방식은 출입구에서 발생하는 보행자의 다양한 움직임 변이에도 안정적인 검출 성능을 보였다. 이러한 방식의 재실자 분석은 건물의 출입구 별로 진행되며 전체적으로 빌딩 내 들어오는 인원과 나가는 인원을 합산하여 빌딩 내 전체 재실자를 추정하는 방식이다. 실험 결과 제안하는 물체의 동선 검출 알고리즘을 YoloV5 분석 후에 적용하였더니 실시간 고속 처리가 가능하였고 기존 중앙선 검출 방식보다 향상된 검출 성능을 확인하였다.

A technology that automatically detects the number of people entering and exiting a building is necessary for the evacuation of victims in the event of a disaster such as a fire. As a solution for this, this paper proposes a method of automatically detecting the volume area of entry and exit personnel in real time using a thermal imaging camera installed at the entrance. The first step of such a detection system is to predict the exact number of people in an image, and the YoloV5 deep learning module was applied in this paper. The next step is the moving direction analysis algorithm of the thermal imaging object proposed in this paper. This algorithm analyzes the sequence of sequentially input thermal volume images and counts movement by distinguishing between incoming and outgoing people. The proposed algorithm divides the main area where the occupant moves in the image into 4 subareas and tracks the shape of the object volume and distribution change in each subarea. The proposed method showed stable detection performance even with various movement variations of pedestrians occurring at the entrance. The occupant analysis in this way is performed for each entrance of the building, and the total number of occupants in the building is estimated by adding the number of people entering and leaving the building as a whole. As a result of the experiment, when the proposed motion detection algorithm of the object was applied after YoloV5 analysis, real-time high-speed processing was possible and improved detection performance was confirmed compared to the existing centerline detection method.

LiDAR 측량은 고밀도로 정확하게 거리를 측정하는 장점 때문에 지표면과 지표면 위의 객체를 3D 모델링하는데 사용되는 주요기술 중의 하나이다. 본 연구의 목적은 고밀도 LiDAR 데이터와 RANSAC 알고리즘을 이용하여 자동으로 철도전력선을 탐지하고 모델링하는 방법을 개발하는데 있다. 철도전력선을 탐지하기 위하여 레이저 데이터의 다중반사 특성과 철도전력선에 대한 형상정보를 이용한다. 이를 위한 프로세스는 최초 단위라인을 찾기 위한 직육면체 분석과 라인 추적, 연결 그리고 색인 작업으로 구성되며, 반복 RANSAC과 라인 파라미터를 구하기 위한 최소제곱법이 모델링을 위하여 사용된다. 철도전력선의 경우에는 정확도 확인을 위한 실측자료를 구하는 것이 매우 힘들어서 정량적인 정확도 평가가 어려우나 모델에 대한 레이저점군의 표준편차는 x-y 및 z 좌표 각각 8cm와 5cm로 양호하였고, 육안 검사에 의한 완성도면에서도 원 데이터와 비교할 때 모든 철도전력선 라인이 탐지 및 모델링된 것을 알 수 있었다. 본 연구에서 제시하는 방법의 모든 과정은 완전히 자동화하였으며, 특히 다수의 전력선이 복잡하게 설치된 지역에서도 적용될 수 있도록 개발하였다.

LiDAR has been one of the widely used and important technologies for 3D modeling of ground surface and objects because of its ability to provide dense and accurate range measurement. The objective of this research is to develop a method for automatic detection and modeling of railroad power lines using high density LiDAR data and RANSAC algorithms. For detecting railroad power lines, multi-echoes properties of laser data and shape knowledge of railroad power lines were employed. Cuboid analysis for detecting seed line segments, tracking lines, connecting and labeling are the main processes. For modeling railroad power lines, iterative RANSAC and least square adjustment were carried out to estimate the lines parameters. The validation of the result is very challenging due to the difficulties in determining the actual references on the ground surface. Standard deviations of 8cm and 5cm for x-y and z coordinates, respectively are satisfactory outcomes. In case of completeness, the result of visual inspection shows that all the lines are detected and modeled well as compare with the original point clouds. The overall processes are fully automated and the methods manage any state of railroad wires efficiently.