• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2356-2363
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• 2015

Localization of lightning strike point (LSP) in the forest is modeled to mitigate the forest fire damage. Though forest fire ignited by lightning rarely happens, its damage on the forest is grievousness. Therefore, predicting accurate location of LSP becomes crucial in order to control the forest fire. In this paper, we proposed a new hybrid localization algorithm by combining the received signal strength (RSS) and the received signal strength ratio (RSSR) to improve the accuracy by mitigating the environmental effect of lightning strike location in the forest. The proposed hybrid algorithm employs antenna theory (AT) model of cloud-to-ground (CG) return stroke lightning channel to forecast the location of the lightning strike. The obtained results show that the proposed hybrid algorithm achieves better location accuracy compared to the existing RSS method for predicting the lightning strike location considering additive white Gaussian noise (AWGN) environment.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.204-204
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• 2010

A development of device with reduced size and improved sensitivity is highly impotant Pb(Zr,Ti)$O_3$ thin films are widely used both to make actuator and sensor due to their high sensitivity and low cost. In this study, the feasibility of a piezoelectric presssure sensors based on hybrid low-temperaute co-fired ceramic (LTCC) technology were presented. The LTCC diaphragms with thickness of $400\;{\mu}m$ were fabricated by laminating 4 green tapes which consist of alumina and glass particle in an organic binder. PZT thin films were successfully prepared on between top and bottom Au electrode with LTCC substrates using RF magnetron sputtering. In addition, The frequency response characteristics of the sensor under varing pressure has been analysed. by Network Analyser (HP-8722D). A frequency shift range has been obseved from 1.7GHz to 1.8GHz with a good linearity for applied pressure from 0 psi up to 25 psi.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.32-38
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• 2012

Each sensor node can know its location in several ways, if the node process the information based on its geographical position in sensor networks. In the localization scheme using GPS, there could be nodes that don't know their locations because the scheme requires line of sight to radio wave. Moreover, this scheme is high costly and consumes a lot of power. The localization scheme without GPS uses a sophisticated mathematical algorithm estimating location of sensor nodes that may be inaccurate. AHLoS(Ad Hoc Localization System) is a hybrid scheme using both GPS and location estimation algorithm. In AHLoS, the GPS node, which can receive its location from GPS, broadcasts its location to adjacent normal nodes which are not GPS devices. Normal nodes can estimate their location by using iterative triangulation algorithms if they receive at least three beacons which contain the position informations of neighbor nodes. But, there are some cases that a normal node receives less than two beacons by geographical conditions, network density, movements of nodes in sensor networks. We propose an indirect localization scheme for low-density sensor nodes which are difficult to receive directly at least three beacons from GPS nodes in wireless network.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.78-87
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• 2011

A variety of intelligent application using the sensor network system is being studied. In general, the sensor network consists of nodes which equipped with a variety of sensing module and is utilized to collect environment information. Recently, the demands of multimedia data are increasing due to the demands of more detailed environmental monitoring or high-quality data. In this paper, we overcome the limitations of low bandwidth in Zigbee-based sensor networks and propose a routing algorithm for real-time multimedia data transmission. In the previously proposed algorithm for multimedia data transmission occurs delay time of routing setup phase and has a low data transmission speed due to bandwidth limitations of Zigbee. In this paper, we propose the hybrid routing algorithm that consist of Zigbee and Bluetooth and solve the bandwidth problem of existing algorithm. We also propose the disjointed multipath setup algorithm based on competition that overcome delay time of routing setup phase in existing algorithm. To evaluate the superiority of the proposed algorithm, we compare it with the existing algorithm. Our experimental results show that the latency was reduced by approximately 78% and the communication speed is increased by approximately 6.9-fold.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.11-22
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• 2010

In case that group communication protocols assume every process is interested in all events occurring in a large scale group, the events multicasting to a subset of a large process group, such as a sensor network, potentially varying for every event based on their interests might lead to very high communication overhead on each individual process. Moreover, despite the importance of both guaranteeing message delivery order and supporting overlapped group using gossip based group communication for multicasting in sensor or P2P networks, there exist little research works on development of gossip-based protocols to satisfy all these requirements. In this paper, we present a new gossip-based causal message order guaranteeing multicast protocol based on local views and delegates representing subgroups and fully utilizing multi-group features to improve scalability. In the proposed protocol, the message delivery order in overlapped groups has been guaranteed consistently by all corresponding members of the groups including delegates. Therefore, these features of the proposed protocol might be significantly scalable rather than those of the protocols guaranteeing atomic order dependencies between multicast messages by hierarchical membership list of dedicated groups like traditional committee protocols and much stronger rather than fully decentralized protocols guaranteeing dependencies between multicast messages based on only local views. And the proposed protocol is a hybrid approach improving the inherent scalability of multicast nature by gossip-based technique in all communications.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.171-182
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• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.