• Smart Structures and Systems
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• v.8 no.3
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• pp.275-302
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• 2011

This is a review paper on mathematical modeling of actively controlled piezo smart structures. Paper has four sections to discuss the techniques to: (i) write the equations of motion (ii) implement sensor-actuator design (iii) model real life environmental effects and, (iv) control structural vibrations. In section (i), methods of writing equations of motion using equilibrium relations, Hamilton's principle, finite element technique and modal testing are discussed. In section (ii), self-sensing actuators, extension-bending actuators, shear actuators and modal sensors/actuators are discussed. In section (iii), modeling of thermal, hygro and other non-linear effects is discussed. Finally in section (iv), various vibration control techniques and useful software are mentioned. This review has two objectives: (i) practicing engineers can pick the most suitable philosophy for their end application and, (ii) researchers can come to know how the field has evolved, how it can be extended to real life structures and what the potential gaps in the literature are.

• Journal of Sensor Science and Technology
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• v.17 no.2
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• pp.106-113
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• 2008

Perceiving a 3D moving target is important for automated machines including robots in a factory. Although stereovision or ultrasonic ranging is often employed for the purpose, the former requires high-cost systems, and the latter suffers from low-resolution. In this paper, we propose a PSD sensor array to acquire the 3D profile of an object conveyed by a belt. Rectangular parallelepiped target objects at random positions and orientations are assumed. Nine PSD sensors are configured in a $3{\times}3$ array above the belt, and fired in a sequence so that cross-talk can be avoided. Time gaps among sensor readings are compensated to get a result that is obtainable when sensors' measurements are made simultaneously along a sensing line. The system designed is tested with various objects in experiments.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.189-194
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• 2001

This paper presents a mew design of an inductive position sensing system with high sensitivity. The designed system consists of the driving coils, position-detecting coils, and closed loop formed magnetic blocks. To obtain high sensitivity we design a symmetric and closed loop type measuring system with small air-gaps. The elements that affect the system characteristics are turn ratio, excitation frequency, air-gap size, capacitance effect, and load resistance. By experimental investigation, the influences of these elements are examined and the system parameters are selected. The sensitivity of the newly designed system is greater than 2800mV/(V mm) and the linearity error is below${\pm}0.01%; in; the; range; of; {\pm}200{\mu}m$.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.113-120
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• 2005

Oceanographic measurements from Ieodo Ocean Research Station and its vicinity were compared for assessment and mutually adjusted with satellite data. From the Topex/Poseidon and ERS-1/2 radar altimeter and scatterometer data, sea surface height, wind speed and direction were extracted and analyzed. Shipborne wind direction data acquired in June 1995 show good coherence with the satellite data, while sea surface height and wind speed show differences, possibly resulting from the distance between the measurement points. This can be improved by analyzing more satellite data or using other available shipborne data. The recent 3 months of Ieodo Station data between December 2004 and February 2005 were also analyzed and compared with the satellite data. The Ieodo Station data were found to have considerable gaps during the period as well as seriously biased particular when the data were averaged with some abnormal data. The Ieodo Station and satellite data were then mutually adjusted on the basis of their statistics. Ieodo Station oceanographic measurements are very efficient for ground-frothing of satellite data because they are stationary and the station is located far from the coast. On the other hand, the satellite measurements are the only data to fill up gaps and adjust biases of the Ieodo Station data.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.228-240
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• 2023

Growing complexity in ecosystem structure and functions, under impacts of climate and land-use changes, requires interdisciplinary understandings of processes and the whole-system, and accurate estimates of the changing functions. In the last three decades, observation networks for biodiversity, ecosystems, and ecosystem functions under climate change, have been developed by interested scientists, research institutions and universities. In this paper we will review (1) the development and on-going activities of those observation networks, (2) some outcomes from forest carbon cycle studies at our super-site "Takayama site" in Japan, and (3) a few ideas how we connect in-situ and satellite observations as well as fill observation gaps in the Asia-Oceania region. There have been many intensive research and networking efforts to promote investigations for ecosystem change and functions (e.g., Long-Term Ecological Research Network), measurements of greenhouse gas, heat, and water fluxes (flux network), and biodiversity from genetic to ecosystem level (Biodiversity Observation Network). Combining those in-situ field research data with modeling analysis and satellite remote sensing allows the research communities to up-scale spatially from local to global, and temporally from the past to future. These observation networks oftern use different methodologies and target different scientific disciplines. However growing needs for comprehensive observations to understand the response of biodiversity and ecosystem functions to climate and societal changes at local, national, regional, and global scales are providing opportunities and expectations to network these networks. Among the challenges to produce and share integrated knowledge on climate, ecosystem functions and biodiversity, filling scale-gaps in space and time among the phenomena is crucial. To showcase such efforts, interdisciplinary research at 'Takayama super-site' was reviewed by focusing on studies on forest carbon cycle and phenology. A key approach to respond to multidisciplinary questions is to integrate in-situ field research, ecosystem modeling, and satellite remote sensing by developing cross-scale methodologies at long-term observation field sites called "super-sites". The research approach at 'Takayama site' in Japan showcases this response to the needs of multidisciplinary questions and further development of terrestrial ecosystem research to address environmental change issues from local to national, regional and global scales.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of Environmental Impact Assessment
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• v.2 no.2
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• pp.73-83
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• 1993

Increasing concern for the environment in Korea has led to the demand that major policies and large-scale development projects be subjected to detailed impact assessment. This paper reports on the state of data related to the prediction of the environmental impact (EIA) to emphasize the importance of data quality. Environmental impact statements (EIS) consulted with the Ministry of Environment of Korea were analyzed from 1981 through 1992. Many of assessors used existing data and collected supplementary data from field survey. Most of the results of EIA are presented directly or summarized on maps and as graphics. For the national purpose, large source of quality-controlled data such as atmospheric data have been developed, However, there are the deficiency in data to analyze the impact of human activity, and data gaps and incompatibilities among systems. Consequently, the development of data bank systems including computer database and remotely-sensed satellite data is required to improve the quality of data which are relevant to EIA. The data bank system should be organized meaningfully in minimum time with a least cost, and measurement standards must be made explicit. Geographical information systems (GIS) are applicable to the graphic presentation or to the impact prediction model.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.206-211
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• 2021

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.31-42
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• 2021

Several studies have been conducted on market orientation over the last three decades. However, the majority of previous research focused exclusively on an internal vision that conceives the market orientation from an organizational perspective, considering the market orientation as a strictly perceived culture or behavior by company's staff (managers and employees) .This study aims to emphasize the importance of analyzing the market orientation from a dual perspective by investigating simultaneously the perceptions of customers and those of managers. It examines the perceptual gap or perceptual congruence of market orientation between customers and managers. A survey is conducted with Tunisian bank managers and B to B customers to measure their market orientation perception. The results should reveal level of manager's market orientation in Tunisian banks compared to customers' perceptions. The perception gaps of market orientation between managers and customers named congruence is highlighted and categorized. This study provides some contributions to fill the gap emerging from the one-sidedness of market orientation evaluation and gives a dyadic vision of market orientation that helps managers in their continuous learning about markets and sensing customers' needs and expectations. Market orientation level between the two groups is evaluated to give some managerial recommendations.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.