• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.2
• /
• pp.46-56
• /
• 2012

Recently, when software architecture is designed, the efficiency of reusability is emphasized. The reusability of the design can raise the quality of GIS software, and reduce the cost of maintenance. Because the object oriented GoF design pattern provides the class hierarchy that can represent repetitively, the importance is emphasized more. This method that designs the GIS software can be applied from various application systems. A multiple distributed sensor network system is composed of the complex structure that each node of the sensor network nodes has different functions and sensor nodes and server are designed by the combination of many classes. Furthermore, this sensor network system may be changed into more complex systems according to a particular purpose of software designer. This paper will design the CATL model by applying Facade pattern that can enhance the efficiency of reuse according to attributes and behaviors in classes in order to implement the complicated structure of the multiple distributed sensor network system based on TinyOS. Therefore, our object oriented GIS design pattern model will be expected to utilize efficiently for design, update, or maintenance, etc. of new systems by packing up attributes and behaviors of classes at complex sensor network systems.

• Korean Journal of Environmental Agriculture
• /
• v.29 no.1
• /
• pp.72-76
• /
• 2010

We have developed the bio-electrode measuring the variance of the amount of acetylcholine affected by residual pesticide. The working electrode of the biosensor was made by combination of cobalt phthalocyanine and carbon organic compounds. The biosensors were constructed by screen-printing method. The principle of working electrode is similar to thiocholine sensor. We have fabricated the biosensor using standard screen printing method. Generally, the biosensor made by printing method formed thick film biosensor. When the electrodes were made by electrochemical cells, the generation of current by the addition of enzyme substrate was inhibited by standard solutions of organo-phosphate pesticides. The detection limit of sensor is about 0.5 $\mu{g}/L$ for carbofuran. We could improve the responsibility of the sensor by controlling the cobalt phthalocyanine and thiocholine concentration ratio. Also we have tested the EPN and Chlorpyrifos pesticides and found that the biosensor is applicable to fast determination of residual pesticides.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.7
• /
• pp.2414-2434
• /
• 2015

Among numerous localization schemes proposed specifically for Wireless Sensor Network (WSN), the range-free localization algorithms based on the received signal strength indication (RSSI) have attracted considerable research interest for their simplicity and low cost. As a typical range-free algorithm, Approximate Point In Triangulation test (APIT) suffers from significant estimation errors due to its theoretical defects and RSSI inaccuracy. To address these problems, a novel localization method called FIAPIT, which is a combination of an improved APIT (IAPIT) and a fuzzy logic system, is proposed. The proposed IAPIT addresses the theoretical defects of APIT in near (it's defined as a point adjacent to a sensor is closer to three vertexes of a triangle area where the sensor resides simultaneously) and far (the opposite case of the near case) cases partly. To compensate for negative effects of RSSI inaccuracy, a fuzzy system, whose logic inference is based on IAPIT, is applied. Finally, the sensor's coordinates are estimated as the weighted average of centers of gravity (COGs) of triangles' intersection areas. Each COG has a different weight inferred by FIAPIT. Numerical simulations were performed to compare four algorithms with varying system parameters. The results show that IAPIT corrects the defects of APIT when adjacent nodes are enough, and FIAPIT is better than others when RSSI is inaccuracy.

• Journal of KIISE
• /
• v.41 no.9
• /
• pp.686-698
• /
• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.1
• /
• pp.117-124
• /
• 2011

This study was realized the home network system for home care by bio-physical sensor system, to convey for the remote physical signal. The composition condition has four functions of displacement point for a Vision, Somatosensory, Vestibular and CNS that the basic measurement used to a Heart Rate, Temperature, Weight. Physical signal are decided to search a max and min point with adjustment of 0.01 unit in the reference level. There were checked physical condition of body balance to compounded a physical neuroceptor of sensory organ for the measurement such as a Vision, Somatosensory, Vestibular, CNS, BMI. There are to check a health care condition through a combination of physical organ with a posturography of a exercise. The service of home network system can be used to support health care management system through health assistants in health care center and central health care system. It was expected to monitor a physical parameter for the remote control health management system.

• Journal of Korean Society for Geospatial Information Science
• /
• v.18 no.4
• /
• pp.3-10
• /
• 2010

In this study, the orbit-based sensor modeling is applied to the digital plotting and the accuracy of digital plotting is analyzed. The KOMPSAT-2 satellite image with orbit-attitude model is used for the analysis. The precise sensor modeling with various combination of parameters is performed for the stereo satellite image. In addition, we analyze the error range of ground control points by applying the result of stereo modeling to digital survey system. According to the result, it is possible to produce digital map using stereo image with a small number of GCPs when the orbit-based sensor modeling for KOMPSAT-2 is applied. This means that it is suitable for the generation of digital map on a scale of 1/5,000 to 1/25,000 considering the resolution of KOMPSAT-2 image.

• Journal of Communications and Networks
• /
• v.17 no.2
• /
• pp.184-197
• /
• 2015

This paper builds on a recent method, chain routing with even energy consumption (CREEC), for designing a wireless sensor network with chain topology and for scheduling the communication to ensure even average energy consumption in the network. In here a new suboptimal design is proposed and compared with the CREEC design. The chain topology in CREEC is reconfigured after each group of n converge-casts with the goal of making the energy consumption along the new paths between the nodes in the chain as even as possible. The new method described in this paper designs a single near-optimal Hamiltonian circuit, used to obtain multiple chains having only the terminal nodes different at different converge-casts. The advantage of the new scheme is that for the whole life of the network most of the communication takes place between same pairs of nodes, therefore keeping topology reconfigurations at a minimum. The optimal scheduling of the communication between the network and base station in order to maximize network lifetime, given the chosen minimum length circuit, becomes a simple linear programming problem which needs to be solved only once, at the initialization stage. The maximum lifetime obtained when using any combination of chains is shown to be upper bounded by the solution of a suitable linear programming problem. The upper bounds show that the proposed method provides near-optimal solutions for several wireless sensor network parameter sets.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.9
• /
• pp.2197-2202
• /
• 2014

In this paper, we report on an investigation of highly sensitive sensing performance of a hydrogen sensor composed of palladium (Pd) nanowires. The Pd nanowires have been grown by electrodeposition into nanochannels and liberated from the anodic aluminum oxide (AAO) template by dissolving in an aqueous solution of NaOH. A combination of photo-lithography, electron beam lithography and a lift-off process has been utilized to fabricate the sensor using the Pd nanowire. The hydrogen concentrations for 2% and 0.1% were obtained from the sensitivities (${\Delta}R/R$) for 1.92% and 0.18%, respectively. The resistance of the Pd nanowires depends on absorption and desorption of hydrogen. Therefore, we expect that the Pd nanowires can be applicable for detecting highly sensitive hydrogen gas at room temperature.

• Journal of Information Processing Systems
• /
• v.11 no.3
• /
• pp.370-388
• /
• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.20-29
• /
• 2021

In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.