• Smart Structures and Systems
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• v.6 no.5_6
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• pp.731-748
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• 2010

Impact detection and health monitoring are very important tasks for civil infrastructures, such as bridges. Piezoceramic based transducers are widely researched for these tasks due to the piezoceramic material's inherent advantages of dual sensing and actuation ability, which enables the active sensing method for structural health monitoring with a network of piezoceramic transducers. Wireless sensor networks, which are easy for deployment, have great potential in health monitoring systems for large civil infrastructures to identify early-age damages. However, most commercial wireless sensor networks are general purpose and may not be optimized for a network of piezoceramic based transducers. Wireless networks of piezoceramic transducers for active sensing have special requirements, such as relatively high sampling rate (at a few-thousand Hz), incorporation of an amplifier for the piezoceramic element for actuation, and low energy consumption for actuation. In this paper, a wireless network is specially designed for piezoceramic transducers to implement impact detection and active sensing for structural health monitoring. A power efficient embedded system is designed to form the wireless sensor network that is capable of high sampling rate. A 32 bit RISC wireless microcontroller is chosen as the main processor. Detailed design of the hardware system and software system of the wireless sensor network is presented in this paper. To verify the functionality of the wireless sensor network, it is deployed on a two-story concrete frame with embedded piezoceramic transducers, and the active sensing property of piezoceramic material is used to detect the damage in the structure. Experimental results show that the wireless sensor network can effectively implement active sensing and impact detection with high sampling rate while maintaining low power consumption by performing offline data processing and minimizing wireless communication.

• Journal of Korea Association of Health Promotion
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• v.3 no.2
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• pp.137-146
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• 2005

The Community based cancer screening program passed in 1960 was a milestone for initiating a national and local health program in Japan. And since then local governments and Cancer Society have been developing and providing cancer screening programs of Stomach, Cervix, Breast and Colorectum for population. To apply the effectiveness of community based cancer screening program, it is important to understand the key issue related to cancer screening participation of population and technology of cancer detection. The purpose of this study was to understand the community based cancer screening program in Japan, and to apply the information for establishment of community based cancer screening program in Korea. The characteristics of community based cancer screening program in Japan were as follows. The first, community based cancer screening program was implemented by the National Health and Medical Services Law for the Aged since 1983. The second, Cancer Society and Cancer Detection Center were core for cancer screening program. The third, the budget for cancer screening program was established by the National Health and Hygiene. The fourth, the continuous quality control for medical staff was provided by Cancer Society and Cancer Detection Center The fifth, the efforts for the promotion of cancer screening rate.

• Clinical Endoscopy
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• v.56 no.5
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• pp.553-562
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• 2023

Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrow-band wavelength light, such as narrow-band imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To gain a better understanding of the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.

• Smart Structures and Systems
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• v.20 no.1
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• pp.43-52
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• 2017

Structural health monitoring has drawn great attention in the field of civil engineering in past two decades. These structural health monitoring methods evaluate structural integrity through high-quality sensor measurements of structures. Due to electronic deterioration or aging problems, sensors may yield biased signals. Therefore, the objective of this study is to develop a fault detection method that identifies malfunctioning sensors in a sensor network. This method exploits the autoregressive modeling technique to generate a bank of Kalman estimators, and the faulty sensors are then recognized by comparing the measurements with these estimated signals. Three types of faults are considered in this study including the additive, multiplicative, and slowly drifting faults. To assess the effectiveness of detecting faulty sensors, a numerical example is provided, while an experimental investigation with faults added artificially is studied. As a result, the proposed method is capable of determining the faulty occurrences and types.

• Smart Structures and Systems
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• v.2 no.3
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• pp.275-293
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• 2006

For health monitoring purpose usually the structure is instrumented with a large scale and multichannel measurement system. In case of highway bridges, operating vehicle could be utilized to reduce the number of measuring devices. First this paper presents a static damage detection algorithm of using operating vehicle load. The technique has been validated by finite element simulation and simple laboratory test. Next the paper presents an approach of using this technique to field application. Here operating vehicle load data has been used by instrumenting the bridge at single location. This approach gives an upper hand to other sophisticated global damage detection methods since it has the potential of reducing the measuring points and devices. It also avoids the application of artificial loading and interruption of any traffic flow.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.407-408
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• 2007

Robust pitch estimation is an important study in many areas of speech processing. In voice pathology, diverse statistics extracted form pitch were commonly used to test voice quality. In this study, we compared several established pitch detection algorithms (PDAs) for verification of adequacy of the PDAs. In the database of total pathological voices of 99 and normal voices of 30, an analysis of errors related with pitch detection was evaluated between pathological and normal voices, or among the types of pathological voices such as benign vocal fold lesions; polyp, nodule, and cysts. Consequently, it is required to survey the severity of tested voice in order to obtain accurate pitch estimates.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.135-149
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• 2011

In this paper, we propose the high-risk group detection model for patients with high blood pressure using case-based reasoning. The proposed model can be applied for public health maintenance organizations to effectively manage knowledge related to high blood pressure and efficiently allocate limited health care resources. Especially, the focus is on the development of the model that can handle constraints such as managing large volume of data, enabling the automatic learning to adapt to external environmental changes and operating the system on a real-time basis. Using real data collected from local public health centers, the optimal high-risk group detection model was derived incorporating optimal parameter sets. The results of the performance test for the model using test data show that the prediction accuracy of the proposed model is two times better than the natural risk of high blood pressure.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.377-380
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• 1996

The purpose of this paper is to improve the P-wave detection capacity using wavelet transform. The first procedure is to remove baseline drift using the median filter. The second procedure is to cancel ECG's QRS-T complex with ECG's QRS-T complex templete to get P-wave candidate. Before we cancelled out the QRS-T complex, we estimated the best matching between templete and QRS-T complex to minimize the error. Then, Harr wavelet was used to eleminate the high frequency noise of ECG wave form cancelled the QRS-T complex. Finally, P-wave was discriminated and confirmed by threshold value. By using this method, We can got the around 95.1% P-wave detection.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of Marine Bioscience and Biotechnology
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• v.6 no.1
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• pp.1-7
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• 2014

Marine-derived pathogens threat health and life of human and animals. Therefore, rapid and specific detection methods need to be developed. Here, we summarized various groups of detection methods, including conventional method, flow cytometry, nucleic acid-based method, and protein-based method. In addition, perspective of detection technique was discussed as a unified detection system for pathogens.