• International journal of advanced smart convergence
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• v.7 no.4
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.10
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• pp.101-106
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• 2013

This paper proposes the new high precision monitoring sensor for the Marine Installation. Among variety of sensor network systems, wireless information transmission through the marine is one of the enabling technologies for the development of future marine-observation systems and sensor networks. Applications of marine monitoring range from oil industry to aquaculture, and include instrument monitoring, pollution control, climate recording, prediction of natural disturbances. For these marine applications to be available, however, the provision of precise location information using monitoring sensor is essential. In this paper, the dynamic characteristics for obtaining the location information of monitoring sensor is analyzed. The theoretical and experimental studies have been carried out. The presented results from the above investigation show considerably excellent performance for the Monitoring for the Marine Installation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.10
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• pp.940-946
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• 2012

This paper proposes a method to predict maximum traction for unmanned robots on rough terrain in order to improve traversability. For a traction prediction, we use a friction-slip model based on modified Brixius model derived empirically in terramechanics which is a function of mobility number $B_n$ and slip ratio S. A friction-slip model includes characteristics of various rough terrains where robots are operated such as soil, sandy soil and grass-covered soil. Using a friction-slip model, we build a prediction model for terrain parameters on which we can know maximum static friction and optimal slip with respect to mobility number $B_n$. In this paper, Mobility number $B_n$ is estimated by modified Willoughby Sinkage model which is a function of sinkage z and slip ratio S. Therefore, if sinkage z and slip ratio are measured once by sensors such as a laser sensor and a velocity sensor, then mobility number $B_n$ is estimated and maximum traction is predicted through a prediction model for terrain parameters. Estimation results for maximum traction are shown on simulation using MATLAB. Prediction Performance for maximum traction of various terrains is evaluated as high accuracy by analyzing estimation errors.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1756-1768
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• 2021

The containment filtered venting system (CFVS) filters the atmosphere of the containment building and discharges a part of it to the outside environment to prevent containment overpressure during severe accidents. The Korean CFVS has a tank that filters fission products from the containment atmosphere by pool scrubbing, which is the primary decontamination process; however, prediction of its performance has been done based on researches conducted under mild conditions than those of severe accidents. Bubble behavior in a pool is a key parameter of pool scrubbing. Therefore, the bubble behavior in the pool was analyzed under various injection flow rates observed at the venturi nozzles used in the Korean CFVS using a wire-mesh sensor. Based on the experimental results, void fraction model was modified using the existing correlation, and a new bubble size prediction model was developed. The modified void fraction model agreed well with the obtained experimental data. However, the newly developed bubble size prediction model showed different results to those established in previous studies because the venturi nozzle diameter considered in this study was larger than those in previous studies. Therefore, this is the first model that reflects actual design of a venturi scrubbing nozzle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.909-937
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• 2011

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decreasing the message complexity. In this paper, a Prediction-based Energy-conserving Approximate StoragE schema (P-EASE) is proposed, which can reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. In addition, focusing on reducing the unnecessary querying messages, P-EASE enables an optimal query algorithm to taking into consideration to query the proper storage node, i.e., the nearer storage node of the centric storage node and local storage node. The theoretical analysis illuminates the correctness and efficiency of the P-EASE. Simulation experiments are conducted under semi-random walk and random waypoint mobility. Compared to EASE, P-EASE performs better at the query error, message complexity, total energy consumption and hotspot energy consumption. Results have shown that P-EASE is more energy-conserving and has higher location precision than EASE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2987-3002
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• 2023

In 2015, the number of senior citizens aged 65 and over in Korea was 6,662,400, accounting for 13.1% of the total population. Along with these social phenomena, risk information related to the elderly is increasing every year. In particular, a fall accident caused by a fall can cause serious injury to an elderly person, so special attention is required. Therefore, in this paper, we implemented a system that monitors fall accidents and informs them in real time to minimize damage caused by falls. To this end, beacon-based indoor location positioning was performed and biometric information based on an integrated module was collected using various sensors. In other words, a multi-functional sensor integration module was designed based on Arduino to collect and monitor user's temperature, heart rate, and motion data in real time. Finally, through the analysis and prediction of measurement signals from the integrated module, damage from fall accidents can be reduced and rapid emergency treatment is possible. Through this, it is possible to reduce the damage caused by a fall accident, and rapid emergency treatment will be possible. In addition, it is expected to lead a new paradigm of safety systems through expansion and application to socially vulnerable groups.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1152-1164
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• 2011

Stock price is stream data to change continuously. The characteristics of these data, stock trends according to flow of time intervals may differ. therefore, stock price should be continuously prediction when the price is updated. In this paper, we propose the new prediction system that continuously predicts the stock price according to the predefined time intervals for the selected stock item using HTM model. We first present a preprocessor which normalizes the stock data and passes its result to the stream sensor. We next present a stream sensor which efficiently processes the continuous input. In addition, we devise a storage node which stores the prediction results for each level and passes it to next upper level and present the HTM network for prediction using these nodes. We show experimented our system using the actual stock price and shows its performance.