• Title/Summary/Keyword: Period Detection

Search Result 1,163, Processing Time 0.035 seconds

A Study on Incident Detection Model using Fuzzy Logic and Traffic Pattern (퍼지논리와 교통패턴을 이용한 유고검지 모형에 관한 연구)

  • Hong, Nam-Kwan;Choi, Jin-Woo;Yang, Young-Kyu
    • Journal of Korea Spatial Information System Society
    • /
    • v.9 no.1
    • /
    • pp.79-90
    • /
    • 2007
  • In this paper we proposed and implemented an incident detection model which combines fuzzy algorithm and traffic pattern in order to enhance the efficiency of incident detection for the highways with lamps. Most of the existing algorithms dealt with highways without lamps and can not be used for detecting incidents in the highways with lamps. The data used for model building are traffic volume, occupancy, and speed data. They have been collected by a loop sensor at 5 minutes interval at a point in the Internal Circular Highway of Seoul for the period of 3 months. In this model, the three parameters collected by sensor were fuzzified and combined with the daily traffic pattern of the link. The test of efficiency of the propsed model was performed by comparing the result of proposed model with traditional APID algorithm and fuzzy algorithm without the pattern data respectively. The result showed significant amount of improvement in reducing the false incident detection rate by 18%.

  • PDF

Value of Combined Detection of Serum CEA, CA72-4, CA19-9 and TSGF in the Diagnosis of Gastric Cancer

  • Yin, Li-Kui;Sun, Xue-Qing;Mou, Dong-Zhen
    • Asian Pacific Journal of Cancer Prevention
    • /
    • v.16 no.9
    • /
    • pp.3867-3870
    • /
    • 2015
  • Background: To explore whether combined detection of serum tumor markers (CEA, CA72-4, CA19-9 and TSGF) improve the sensitivity and accuracy in the diagnosis of gastric cancer (GC). Materials and Methods: An automatic chemiluminescence immune analyzer with matched kits were used to determine the levels of serum CEA, CA72-4, CA19-9 and TSGF in 45 patients with gastric cancer (GC group), 40 patients with gastric benign diseases (GBD group) hospitalized in the same period and 30 healthy people undergoing a physical examination. The values of those 4 tumor markers in the diagnosis of gastric cancer was analyzed. Results: The levels of serum CEA, CA72-4, CA19-9 and TSGF of the GC group were higher than those of the GBD group and healthy examined people and the differences were significant (P<0.001). The area under receiver operating characteristic (ROC) curves for single detection of CEA, CA72-4, CA19-9 and TSGF in the diagnosis of GC was 0.833, 0.805, 0.810 and 0.839, respectively. The optimal cutoff values for these 4 indices were 2.36 ng/mL, 3.06 U/mL, 5.72 U/mL and 60.7 U/mL, respectively. With combined detection of tumor markers, the diagnostic power of those 4 indices was best, with an area under the ROC curve of 0.913 (95%CI 0.866~0.985), a sensitivity of 88.9% and a diagnostic accuracy of 90.4%. Conclusions: Combined detection of serum CEA, CA72-4, CA19-9 and TSGF increases the sensitivity and accuracy in diagnosis of GC, so it can be regarded as the important means for early diagnosis.

Leak Detection and Evaluation for Power Plant Boiler Tubes Using Acoustic Emission (음향방출을 이용한 보일러튜브 누설평가)

  • Lee, Sang-Guk
    • Journal of the Korean Society for Nondestructive Testing
    • /
    • v.24 no.1
    • /
    • pp.45-51
    • /
    • 2004
  • Boiler tubes in power plants are often leaked due to various material degradations including creep and thermal fatigue damage under severe operating conditions such as high temperature and high pressure over an extended period of time. To monitor and diagnose the tubes on site and in real time, the acoustic emission (AE) technology was applied. We developed an AE leak detection system, and used it to study the variation of AE signal from the on-site tubes in response to the changes in the boiler operation condition and to detect the locations of leakage based on it. Detection of leak was performed by acquiring and evaluating the signals in separate regimes of high and low frequency signal. As a result of these studies, we found that on-line monitoring and detection of leak location for boiler tubes is possible using the developed system. Thus, the system is expected to contribute to the safe operation of power plants, and prevent economic losses due to potential leak.

Comparison of Atmospheric River Detection Algorithms in East Asia (동아시아 대기의 강 탐지 알고리즘 비교)

  • Gyuri Kim;Seung-Yoon Back;Yeeun Kwon;Seok-Woo Son
    • Atmosphere
    • /
    • v.33 no.4
    • /
    • pp.399-411
    • /
    • 2023
  • This study compares the three detection algorithms of East Asian summer atmospheric rivers (ARs). The algorithms developed by Guan and Waliser (GW15), Park et al. (P21), and Tian et al. (T23) are particularly compared in terms of the AR frequency, the number of AR events, and the AR duration for the period of 2016-2020. All three algorithms show similar spatio-temporal distributions of AR frequency, centered along the edge of the North Pacific high. The maximum AR frequency gradually shifts northward in early summer as the edge of the North Pacific High expands, and retreats in late summer. However, the detailed pattern and the maximum value differ among the algorithms. When the AR frequency is decomposed into the number of AR events and the AR duration, the AR frequencies detected by GW15 and P21 are equally explained by both factors. However, the number of AR events primarily determine the AR frequency in T23. This difference occurs as T23 utilizes the machine learning algorithm applied to moisture field while GW15 and P21 apply the threshold value to moisture transport field. When evaluating AR-related precipitation, the ARs detected by P21 show the closest relationship with total precipitation in East Asia by up to 60%. These results indicate that AR detection in the East Asian summer is sensitive to the choice of the detection algorithm and can be optimized for the target region.

Financial Fraud Detection using Data Mining: A Survey

  • Sudhansu Ranjan Lenka;Bikram Kesari Ratha
    • International Journal of Computer Science & Network Security
    • /
    • v.24 no.9
    • /
    • pp.169-185
    • /
    • 2024
  • Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

Spatio-temporal change detection of land-use and urbanization in rural areas using GIS and RS - Case studies of Yongin and Anseong regions - (GIS와 RS를 이용한 농촌지역 토지이용 및 도시화 변화현상의 시공간 탐색 - 용인 및 안성지역을 중심으로 -)

  • Gao, Yujie;Kim, Dae-Sik
    • Korean Journal of Agricultural Science
    • /
    • v.38 no.1
    • /
    • pp.153-162
    • /
    • 2011
  • This study analyzed the spatio-temporal change detection of land-use and urbanization in Yongin and Anseong regions, Kyunggi Province, using three Landsat-5 TM images for 1990, 1996, and 2000. Remote sensing (RS) and geographic information system (GIS) techniques were used for image classification and result analysis. Six land-use types were classified using supervised maximum likelihood classification. In the two study areas, the land-use changed significantly, especially the decrease of arable land and forest and increase of built-up area. Spatially, the urban expansion of Yongin region showed a spreading trend mainly along the national road and expressways. But in Anseong region the expansion showed 'urban sprawl phenomenon' with irregular shape like starfish. Temporally, the urban expansion showed disparity - the growth rates of urbanized area rose from the period 1990-1996 to 1996-2000 in both study areas. The increased built-up areas were converted mainly from paddy, dry vegetation, and forest.

Impact of Voice Activity Detection on Channel Allocation in Cellular Networks

  • Limsaksri, Wichan;Thipchaksurat, Sakchai;Varakulsiripunth, Ruttikorn
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 2004.08a
    • /
    • pp.1067-1071
    • /
    • 2004
  • In this paper, the performance enhancement algorithm of channel allocation for voice and data transmission in cellular networks is proposed. The voice activity detection has been applied to dynamic channel allocation procedure to detect and separate the silence and speech among conversation periods. Hence a data user can use the silent period of an active voice channel to transmit its information. To control the selecting of channel allocation policies, the information of number of data in transmission waiting queue has been determined in order to accept the performance measurement. In the simulation results, the improvement of the performance shows via the quality of services, which are an average delay in queue, a blocking probability, and an impact of the proposed scheme is presented in the system.

  • PDF

Call for a Computer-Aided Cancer Detection and Classification Research Initiative in Oman

  • Mirzal, Andri;Chaudhry, Shafique Ahmad
    • Asian Pacific Journal of Cancer Prevention
    • /
    • v.17 no.5
    • /
    • pp.2375-2382
    • /
    • 2016
  • Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

A Survey of Intestinal Protozoan Infections among Gastroenteritis Patients during a 3-Year Period (2004-2006) in Gyeonggi-do (Province), South Korea

  • Huh, Jeong-Weon;Moon, Su-Gyeong;Lim, Young-Hee
    • Parasites, Hosts and Diseases
    • /
    • v.47 no.3
    • /
    • pp.303-305
    • /
    • 2009
  • The incidence and etiology of parasite-associated gastroenteritis during 2004-2006 in Gyeonggi-do (province), South Korea was determined by means of antigen detection ELISA on 6,071 stool specimens collected from 6 general hospitals. At least 1 parasitic agent was detected in 3.4% (208/6,071) of the stool samples. Among these, Giardia lamblia was the most numerous (152 cases; 2.5%), followed by Entamoeba histolytica (25 cases; 0.4%), Cryptosporidium parvum (23 cases; 0.4%), and mixed infections (8 cases; 0.1%). Patients aged 1-5years had the largest proportion (69.2%; 144/208) of parasite-positive stool specimens. Parasite-mediated gastroenteritis was most common from June to September. The detection rate gradually increased from 2004 to 2006. This study shows that parasite-mediated gastroenteritis may be significant among children in Korea and that parasite infection surveillance should be constantly performed.

An Efficient Voice Activity Detection Method using Bi-Level HMM (Bi-Level HMM을 이용한 효율적인 음성구간 검출 방법)

  • Jang, Guang-Woo;Jeong, Mun-Ho
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.10 no.8
    • /
    • pp.901-906
    • /
    • 2015
  • We presented a method for Vad(Voice Activity Detection) using Bi-level HMM. Conventional methods need to do an additional post processing or set rule-based delayed frames. To cope with the problem, we applied to VAD a Bi-level HMM that has an inserted state layer into a typical HMM. And we used posterior ratio of voice states to detect voice period. Considering MFCCs(: Mel-Frequency Cepstral Coefficients) as observation vectors, we performed some experiments with voice data of different SNRs and achieved satisfactory results compared with well-known methods.