• Title/Summary/Keyword: sensor prediction

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An Integrated Accurate-Secure Heart Disease Prediction (IAS) Model using Cryptographic and Machine Learning Methods

  • Syed Anwar Hussainy F;Senthil Kumar Thillaigovindan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.2
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    • pp.504-519
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    • 2023
  • Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

Maximizing Information Transmission for Energy Harvesting Sensor Networks by an Uneven Clustering Protocol and Energy Management

  • Ge, Yujia;Nan, Yurong;Chen, Yi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.4
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    • pp.1419-1436
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    • 2020
  • For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

A Study of Non-Intrusive Appliance Load Identification Algorithm using Complex Sensor Data Processing Algorithm (복합 센서 데이터 처리 알고리즘을 이용한 비접촉 가전 기기 식별 알고리즘 연구)

  • Chae, Sung-Yoon;Park, Jinhee
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.2
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    • pp.199-204
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    • 2017
  • In this study, we present a home appliance load identification algorithm. The algorithm utilizes complex sensory data in order to improve the existing NIALM using total power usage information. We define the influence graph between the appliance status and the measured sensor data. The device identification prediction result is calculated as the weighted sum of the predicted value of the sensor data processing algorithm and the predicted value based on the total power usage. We evaluate proposed algorithm to compare appliance identification accuracy with the existing NIALM algorithm.

EXPERIMENTAL STUDY ON THE CHARACTERISTICS OF RIVERBED MATERIALS USING AN ULTRASONIC SENSOR

  • Yeo, Woon-Kwang;Jang, Bok-Jin;Lee, Jong-Kook;Kim, Young-Bin
    • Water Engineering Research
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    • v.7 no.1
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    • pp.21-28
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    • 2006
  • The scouring process is complex and subject to many factors. Recently, experiments for real-time bridge scour monitoring have been active as means for a more reliable scour prediction. Riverbed materials are an important factor in bridge scouring; therefore, an accurate estimation of riverbed material is critical in predicting a scour. As a part of this approach, an ultrasonic sensor, which can not only detect river bottom during floods but can also be installed lose to the underwater structures, was developed. This sensor is able to map the river bottom using an ultrasonic waves with the characteristics of the returning wave, reflected from an object or bottom ground. The reflected wave is unique according to the situations, or materials below. Therefore, it would be possible to identify the consisting materials of a riverbed if we could reveal each characteristic in the received signals. In this study, a preliminary experiment was performed in the laboratory to identify and classify received signals, which is unique to each material. The analysis of this experiment gives the graph, which makes it possible to identify materials of the river bottom through the ultrasonic signals. The proposed graph was verified through a comparison with the actual field data measured in river.

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Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

  • Zou, Dongyao;Sun, Guohao;Li, Zhigang;Xi, Guangyong;Wang, Liping
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.8
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    • pp.2627-2647
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    • 2022
  • The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

Development of Real-time Traffic Information Generation Technology Using Traffic Infrastructure Sensor Fusion Technology (교통인프라 센서융합 기술을 활용한 실시간 교통정보 생성 기술 개발)

  • Sung Jin Kim;Su Ho Han;Gi Hoan Kim;Jung Rae Kim
    • 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.

A Study on Uncertainty Quantification and Performance Confidence Interval Estimation for Application to Digital Twin of Oscillating Water Column Type Wave Power Generator System (진동수주형 파력발전 시스템의 디지털 트윈 적용을 위한 불확실성 정량화 및 성능 신뢰구간 추정 연구)

  • Tae-Kyun Kim;Su-Gil Cho;Jae-Won Oh;Tae-Hee Lee
    • Journal of the Korean Society of Industry Convergence
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    • v.26 no.3
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    • pp.401-409
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    • 2023
  • Oscillating water column (OWC) type wave power generator system is a power generation system that uses wave energy, a sustainable and renewable energy source. Irregular cycles and wave heights act as factors that make it difficult to secure generation efficiency of the wave power generator system. Recently, research for improving power generation efficiency is being conducted by applying digital twin technology to OWC type wave energy converter system. However, digital twin using sensor data can predict erroneous performance due to uncertainty in the sensor data. Therefore, this study proposes an uncertainty analysis method for sensor data which is used in digital twin to secure the reliability of digital twin prediction results. Uncertainty quantification considering sensor data characteristics and future uncertainty information according to uncertainty propagation were derived mathematically, and confidence interval estimation was performed based on the proposed method.

Prediction of Sleep Stages and Estimation of Sleep Cycle Using Accelerometer Sensor Data (가속도 센서 데이터 기반 수면단계 예측 및 수면주기의 추정)

  • Gang, Gyeong Woo;Kim, Tae Seon
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1273-1279
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    • 2019
  • Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

Event Cognition-based Daily Activity Prediction Using Wearable Sensors (웨어러블 센서를 이용한 사건인지 기반 일상 활동 예측)

  • Lee, Chung-Yeon;Kwak, Dong Hyun;Lee, Beom-Jin;Zhang, Byoung-Tak
    • Journal of KIISE
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    • v.43 no.7
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    • pp.781-785
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    • 2016
  • Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

Nondestructive Buckling Load Prediction of Pressurized Unstiffened Metallic Cylinder Using Vibration Correlation Technique (Vibration Correlation Technique을 이용한 내부 압력을 받는 금속재 단순 원통 구조의 비파괴적 전역 좌굴 하중 예측)

  • Jeon, Min-Hyeok;Kong, Seung-Taek;Cho, Hyun-Jun;Kim, In-Gul;Park, Jae-Sang;Yoo, Joon-Tae;Yoon, Yeoung-Ha
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.50 no.2
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    • pp.75-82
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    • 2022
  • Nondestructive method to predict buckling load for the propellant tank of launch vehicle should be evaluated. Vibration correlation technique can predict the global buckling load of unstiffened cylindrical structure with geometric initial imperfection using correlation of natural frequency and compressive load from compressive test below the buckling load. In this study, vibration and buckling tests of a thin metal unstiffened propellant tank model subjected to internal pressure and compressive loads were performed and the test results were used for VCT to predict global buckling load. For the vibration test of thin structure, non-contact excitation method using a speaker was used. The response was measured with piezoelectric polymer(PVDF) sensor. Prediction results of VCT were compared with the measured buckling load in the test and the nondestructive global buckling load prediction method was verified.