• International Journal of Advanced Culture Technology
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• 제5권1호
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• pp.64-69
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• 2017

The rapid growth of algae occurs can induce the algae bloom when nutrients are supplied from anthropogenic sources such as fertilizer, animal waste or sewage in runoff the water currents or upwelling naturally. The algae blooms creates the human health problem in the environment as well as in the water resource managers including hypoxic dead zones and harmful toxins and pose challenges to water treatment systems. The algal blooms in the source water in water treatment systems affects the drinking water taste & odor while clogging or damaging filtration systems and putting a strain on the systems designed to remove algal toxins from the source water. This paper propose the emerging In-Situ self-diagnosable smart algae sensing device with wireless connectivity for smart remote monitoring and control. In this research, we developed the In-Site Algae diagnosable sensing device with wireless sensor network (WSN) connectivity with Optical Biological Sensor and environmental sensor to monitor the water treatment systems. The proposed system emulated in real-time on the water treatment plant and functional evaluation parameters are presented as part of the conceptual proof to the proposed research.

• 농업과학연구
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• 제50권4호
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• pp.883-902
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• 2023

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Short-range sensing can be reliable, rapid, inexpensive, and used for applications based on well-developed and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

• Smart Structures and Systems
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• 제15권2호
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• pp.299-314
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• 2015

Submersed rock-berm structures are frequently used for protection of underwater lifelines such as pipelines and power cables. During the service life, the rock-berm structure can experience several accidental loads such as anchor collision. The consequences can be severe with a certain level of frequency; hence, the structural responses should be carefully understood for implementing a proper structural health monitoring method. However, no study has been made to quantify the structural responses because it is hard to deal with the individual behavior of each rock. Therefore, this study presents a collision analysis of the submersed rock-berm structure using a finite element software package by facilitating the smoothed-particle hydrodynamics (SPH) method. The analysis results were compared with those obtained from the Lagrange method. Moreover, two types of anchors (stock anchor and stockless anchor), three collision points and two different drop velocities (terminal velocity of each anchor and 5 m/s) were selected to investigate the changes in the responses. Finally, the effect of these parameters (analysis method, anchor type, collision point and drop velocity) on the analysis results was studied. Accordingly, the effectiveness of the SPH method is verified, a safe rock-berm height (over 1 m) is proposed, and a gauge point (0.5 m above the seabed) is suggested for a structural health monitoring implementation.

• Smart Structures and Systems
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• 제22권5호
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• pp.587-609
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• 2018

There is greater significance in identifying the incipient damages in structures at the time of their initiation as timely rectification of these minor incipient cracks can save huge maintenance cost. However, the change in the global dynamic characteristics of a structure due to these subtle damages are insignificant enough to detect using the majority of the current damage diagnostic techniques. Keeping this in view, we propose a hybrid damage diagnostic technique for detection of minor incipient damages in the structures. In the proposed automated hybrid algorithm, the raw dynamic signatures obtained from the structure are decomposed to uni-modal signals and the dynamic signature are reconstructed by identifying and combining only the uni-modal signals altered by the minor incipient damage. We use these reconstructed signals for damage diagnostics using ARMAX model. Numerical simulation studies are carried out to investigate and evaluate the proposed hybrid damage diagnostic algorithm and their capability in identifying minor/incipient damage with noisy measurements. Finally, experimental studies on a beam are also presented to compliment the numerical simulations in order to demonstrate the practical application of the proposed algorithm.

• Smart Structures and Systems
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• 제14권5호
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• Smart Structures and Systems
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• 제17권3호
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Smart Structures and Systems
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• 제6권7호
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• pp.811-833
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• 2010

Vibration control and health monitoring of building structures have been actively investigated in recent years but treated separately according to the primary objective pursued. This paper presents a general approach in the time domain for integrating vibration control and health monitoring of a building structure to accommodate various types of control devices and on-line damage detection. The concept of the time-domain approach for integrated vibration control and health monitoring is first introduced. A parameter identification scheme is then developed to identify structural stiffness parameters and update the structural analytical model. Based on the updated analytical model, vibration control of the building using semi-active friction dampers against earthquake excitation is carried out. By assuming that the building suffers certain damage after extreme event or long service and by using the previously identified original structural parameters, a damage detection scheme is finally proposed and used for damage detection. The feasibility of the proposed approach is demonstrated through detailed numerical examples and extensive parameter studies.

• 한국인공지능학회지
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• 제8권1호
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• pp.1-6
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• 2020

The Recently there has been a growing interest in health care due to the COVID-19 situation. In this paper, we intend to develop a healthcare monitoring system to provide users with smart healthcare systems in line with the healthcare 3.0 era. The system consists of a wireless network between various sensors, Android smartphones, and OLEDs using Bluetooth, and through this, a health care monitoring system capable of collecting user's biometric information and managing health by receiving data values of sensors connected to Arduino. In conclusion, the user's BPM value was calculated using the heart rate sensor, and the exercise intensity can be adjusted through this. In addition, a step derivation algorithm is implemented using an acceleration sensor, and calorie consumption can be measured using the step and weight values. As such, the heart rate, step count, calorie consumption data can be transmitted to a smartphone application through a Bluetooth module and output, and can be output to an OLED for users who are not easy to access the smartphone. This healthcare monitoring system can be applied to various groups and technologies.

• Smart Structures and Systems
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• 제5권4호
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• pp.317-328
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• 2009

Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.

• 한국정보통신학회논문지
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• 제22권11호
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• pp.1421-1427
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• 2018

사물 인터넷 기술의 급속한 발전으로 다양한 종류의 스마트 센서들이 개발 보급되고 있다. 이러한 스마트 센서들은 주로 관리자의 경험에 의해서 관리되던 축산업에도 최근 적용되어 가축 개체에 웨어러블 센서를 달거나 사물인터넷 센서를 갖춘 스마트팜 사용을 통해서 가축관리의 효율성을 향상시키고 있다. 본 논문에서는 목걸이형 스마트 센서를 이용하여 젖소의 체온과 운동량을 측정하고 이를 기반으로 개체의 상태를 파악하는 방안을 개발하였다. 특히 젖소 관리에서 제일 중요한 요소인 젖소의 발정여부를 파악하는 방안을 다양한 머신러닝 방법을 이용하여 분석하였고 이를 통해서 높은 정확도로 발정여부를 예측할 수 있음을 보였다. 제안한 방안의 사용을 통해서 젖소의 발정여부를 빠르게 확인하고 이를 통해서 젖소 관리의 효율성을 향상시킬 수 있다.