• Title/Summary/Keyword: detecting accuracy

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Algorithm for Judging Anomalies Using Sliding Window to Reproduce the Color Temperature Cycle of Natural Light (자연광의 색온도 주기 재현을 위한 슬라이딩 윈도우 기반 이상치 판정 알고리즘)

  • Jeon, Geon Woo;Oh, Seung Taek;Lim, Jae Hyun
    • Journal of Korea Multimedia Society
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    • v.24 no.1
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    • pp.30-39
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    • 2021
  • Research in the field of health lighting has continued to advance to reproduce the color temperature of natural light which periodically changes. However, most of this research could only reproduce a uniform circadian color temperature of natural light, therefore failing to realize the characteristics of the circadian cycle of color temperature difference by latitude and longitude. To reproduce the color temperature of natural light on which the characteristics of a region are reflected, the collection technology of real-time characteristics of natural light is needed. If the color temperatures which are not within a periodical pattern due to climate changes, etc., are measured, it will be difficult to judge the occurrence (presence) of the anomalies and to reproduce the circadian cycle of the color temperature of natural light. Therefore, this study proposes an algorithm for judging the anomalies in real time based on the sliding window to reproduce the color temperature of natural light. First, the natural light characteristics DB collected through the on-site measurement were analyzed, the differential values at a one-minute interval were calculated and examined, and then representative color temperature circadian patterns by solar terms were drawn. The anomalies were then detected by the application of the sliding window that calculated the deviation of the color temperature for the measured color temperature data set, which was collected through RGB sensors, while moving along the time sequence. In addition, the presence of anomalies was verified through the comparison study between the detection results and the representative circadian cycle of the color temperature by solar term. The judgment method for the anomalies from the measured color temperature of natural light was proposed for the first time, confirming that the proposed method was capable of detecting the anomalies with an average accuracy of 94.6%.

Design and Implementation of People Counting System Based Piezoelectric Mat for Simultaneous Passing Pedestrian Counting (동시 통과 보행 인원 계수를 위한 압전매트 기반 인원 계수 시스템 설계 및 구현)

  • Jang, Si-Woong;Cho, Jin-Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.10
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    • pp.1361-1368
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    • 2020
  • The system for counting the number of people has traditionally been implemented in various ways. Existing systems include infrared sensors, lasers, cameras, etc. In the case of such an existing system, there are restrictions on space such as ceilings and sides of walls. In this paper, we propose a method of detecting the footsteps of pedestrians using a piezoelectric mat containing a number of piezoelectric sensors and counting the number of pedestrians passing simultaneously by using the data collected from the piezoelectric mat. When pedestrians pass over piezoelectric mats, the collected sensor data was aggregated using SPI communication and transmitted to PC server using TCP/IP communication. Performance analysis shows that approximately 600 step data can be recognized with 99% accuracy. This is to overcome the shortcomings of other counting systems.

Evaluation of the posterior superior alveolar artery canal by cone-beam computed tomography in a sample of the Egyptian population

  • Fayek, Marco Malak;Amer, Maha Eshak;Bakry, Ahmed Mohamed
    • Imaging Science in Dentistry
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    • v.51 no.1
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    • pp.35-40
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    • 2021
  • Purpose: This study was conducted to evaluate the accuracy of cone-beam computed tomography (CBCT) in detecting the posterior superior alveolar(PSA) artery canal in a sample of the Egyptian population. Materials and Methods: CBCT images of 600 maxillary sinuses of patients were examined for the presence or absence of the PSA artery along the lateral wall of the maxillary sinus, and for the diameter and type of the canal in relation to age and sex. The distances from the canal to the alveolar crest and sinus floor were also measured. Each canal was assessed to determine whether it was bifid. Results: The PSA artery canal could be detected in 92.0% of the sinuses. The mean distance from the inferior border of the PSA artery canal to the sinus floor was 8.2±2.2 mm (range, 3.2-13.6 mm) in males and 7.3±2.1 mm (range, 3.0-13.1 mm) in females. The mean distance from the inferior border of the PSA artery canal to the alveolar crest was 18.2±2.7 mm (range, 11.0-23.9 mm) in males and 17.4±2.3 mm (range, 10.8-23.5 mm) in females. The mean diameter of the PSA artery canal was larger in male subjects. The PSA artery canal was bifid in 8.7% of cases. The most frequently observed location of the PSA artery canal was intraosseous(82.2%). Conclusion: CBCT was confirmed to be a valuable tool for evaluation and localization of the PSA artery before maxillary sinus lift surgery to avoid intraoperative bleeding.

Exploration of deep learning facial motions recognition technology in college students' mental health (딥러닝의 얼굴 정서 식별 기술 활용-대학생의 심리 건강을 중심으로)

  • Li, Bo;Cho, Kyung-Duk
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.3
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    • pp.333-340
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    • 2022
  • The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

Strawberry disease diagnosis service using EfficientNet (EfficientNet 활용한 딸기 병해 진단 서비스)

  • Lee, Chang Jun;Kim, Jin Seong;Park, Jun;Kim, Jun Yeong;Park, Sung Wook;Jung, Se Hoon;Sim, Chun Bo
    • Smart Media Journal
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    • v.11 no.5
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    • pp.26-37
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    • 2022
  • In this paper, images are automatically acquired to control the initial disease of strawberries among facility cultivation crops, and disease analysis is performed using the EfficientNet model to inform farmers of disease status, and disease diagnosis service is proposed by experts. It is possible to obtain an image of the strawberry growth stage and quickly receive expert feedback after transmitting the disease diagnosis analysis results to farmers applications using the learned EfficientNet model. As a data set, farmers who are actually operating facility cultivation were recruited and images were acquired using the system, and the problem of lack of data was solved by using the draft image taken with a cell phone. Experimental results show that the accuracy of EfficientNet B0 to B7 is similar, so we adopt B0 with the fastest inference speed. For performance improvement, Fine-tuning was performed using a pre-trained model with ImageNet, and rapid performance improvement was confirmed from 100 Epoch. The proposed service is expected to increase production by quickly detecting initial diseases.

Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks

  • Yeo, Seung-Yeon;Jo, So-Young;Kim, Jiyeon
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.7
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    • pp.101-108
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    • 2022
  • We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

CALS: Channel State Information Auto-Labeling System for Large-scale Deep Learning-based Wi-Fi Sensing (딥러닝 기반 Wi-Fi 센싱 시스템의 효율적인 구축을 위한 지능형 데이터 수집 기법)

  • Jang, Jung-Ik;Choi, Jaehyuk
    • Journal of IKEEE
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    • v.26 no.3
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    • pp.341-348
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    • 2022
  • Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

Development of Real-time PCR Assay Based on Hydrolysis Probe for Detection of Epichloë spp. and Toxic Alkaloid Synthesis Genes

  • Lee, Ki-Won;Woo, Jae Hoon;Song, Yowook;Rahman, Md Atikur;Lee, Sang-Hoon
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.42 no.3
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    • pp.201-207
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    • 2022
  • Fescues, which are widely cultivated as grasses and forages around the world, are often naturally infected with the endophyte, Epichloë. This fungus, transmitted through seeds, imparts resistance to drying and herbivorous insects in its host without causing any external damage, thereby contributing to the adaptation of the host to the environment and maintaining a symbiosis. However, some endophytes, such as E. coenophialum synthesize ergovaline or lolitrem B, which accumulate in the plant and impart anti-mammalian properties. For example, when livestock consume excessive amounts of grass containing toxic endophytes, problems associated with neuromuscular abnormalities, such as convulsions, paralysis, high fever, decreased milk production, reproductive disorders, and even death, can occur. Therefore, pre-inoculation with non-toxic endogenous fungi or management with endophyte-free grass is important in preventing damage to livestock and producing high-quality forage. To date, the diagnosis of endophytes has been mainly performed by observation under a microscope following staining, or by performing an immune blot assay using a monoclonal antibody. Recently, the polymerase chain reaction (PCR)-based molecular diagnostic method is gaining importance in the fields of agriculture, livestock, and healthcare given the method's advantages. These include faster results, with greater accuracy and sensitivity than those obtained using conventional diagnostic methods. For the diagnosis of endophytes, the nested PCR method is the only available option developed; however, it is limited by the fact that the level of toxic alkaloid synthesis cannot be estimated. Therefore, in this study, we aimed to develop a triplex real-time PCR diagnostic method that can determine the presence or absence of endophyte infection using DNA extracted from seeds within 1 h, while simultaneously detecting easD and LtmC genes, which are related to toxic alkaloid synthesis. This new method was then also applied to real field samples.

Can indirect magnetic resonance arthrography be a good alternative to magnetic resonance imaging in diagnosing glenoid labrum lesions?: a prospective study

  • Mardani-Kivi, Mohsen;Alizadeh, Ahmad;Asadi, Kamran;Izadi, Amin;Leili, Ehsan Kazemnejad;arzpeyma, Sima Fallah
    • Clinics in Shoulder and Elbow
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    • v.25 no.3
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    • pp.182-187
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    • 2022
  • Background: This study was designed to evaluate and compare the diagnostic value of magnetic resonance imaging (MRI) and indirect magnetic resonance arthrography (I-MRA) imaging with those of arthroscopy and each other. Methods: This descriptive-analytical study was conducted in 2020. All patients who tested positive for labrum lesions during that year were included in the study. The patients underwent conservative treatment for 6 weeks. In the event of no response to conservative treatment, MRI and I-MRA imaging were conducted, and the patients underwent arthroscopy to determine their ultimate diagnosis and treatment plan. Imaging results were assessed at a 1-week interval by an experienced musculoskeletal radiologist. Image interpretation results and arthroscopy were recorded in the data collection form. Results: Overall, 35 patients comprised the study. Based on the kappa coefficient, the results indicate that the results of both imaging methods are in agreement with the arthroscopic findings, but the I-MRA consensus rate is higher than that of MRI (0.612±0.157 and 0.749±0.101 vs. 0.449±0.160 and 0.603±0.113). The sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of MRI in detecting labrum tears were 77.77%, 75.00%, 91.30%, 50.00%, and 77.14%, respectively, and those of I-MRA were 88.88%, 75.00%, 92.30%, 66.66%, and 85.71%. Conclusions: Here, I-MRA showed higher diagnostic value than MRI for labral tears. Therefore, it is recommended that I-MRA be used instead of MRI if there is an indication for potential labrum lesions.

Power spectral density method performance in detecting damages by chloride attack on coastal RC bridge

  • Mehrdad, Hadizadeh-Bazaz;Ignacio J., Navarro;Victor, Yepes
    • Structural Engineering and Mechanics
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    • v.85 no.2
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    • pp.197-206
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    • 2023
  • The deterioration caused by chloride penetration and carbonation plays a significant role in a concrete structure in a marine environment. The chloride corrosion in some marine concrete structures is invisible but can be dangerous in a sudden collapse. Therefore, as a novelty, this research investigates the ability of a non-destructive damage detection method named the Power Spectral Density (PSD) to diagnose damages caused only by chloride ions in concrete structures. Furthermore, the accuracy of this method in estimating the amount of annual damage caused by chloride in various parts and positions exposed to seawater was investigated. For this purpose, the RC Arosa bridge in Spain, which connects the island to the mainland via seawater, was numerically modeled and analyzed. As the first step, each element's bridge position was calculated, along with the chloride corrosion percentage in the reinforcements. The next step predicted the existence, location, and timing of damage to the entire concrete part of the bridge based on the amount of rebar corrosion each year. The PSD method was used to monitor the annual loss of reinforcement cross-section area, changes in dynamic characteristics such as stiffness and mass, and each year of the bridge structure's life using sensitivity equations and the linear least squares algorithm. This study showed that using different approaches to the PSD method based on rebar chloride corrosion and assuming 10% errors in software analysis can help predict the location and almost exact amount of damage zones over time.