• Journal of Animal Science and Technology
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• v.64 no.5
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• pp.813-829
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• 2022

Internal and external defects of eggs should be detected to prevent cross-contamination of intact eggs by abnormal eggs during storage. Emerging detection technologies for abnormal eggs were introduced as an alternative to human inspection. The advanced technologies could rapidly detect abnormal eggs. Abnormal egg detection technologies using acoustic response, machine vision, and spectroscopy have been commercialized in the poultry industry. Non-destructive egg quality assessment methods meanwhile could preserve the value of eggs and improve detection efficiency. In order to improve detection efficiency, it is essential to select a proper algorithm for classifying the types of abnormal eggs. This review deals with the performance of the detection technologies for various types of abnormal eggs in recently published resources. In addition, the discriminant methods and detection algorithms of abnormal eggs reported in the published literature were investigated. Although the majority of the studies were conducted on a laboratory scale, the developed detection technologies for internal and external defects in eggs were technically feasible to obtain the excellent detection accuracy. To apply the developed detection technologies to the poultry industry, it is necessary to achieve the detection rates required from the industry.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2483-2488
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• 2023

Differential die-away analysis (DDAA) technology is a special nuclear material (SNM) active detection analysis technology. Be a nuclear material shielded or not, the technology can reveal the existence of nuclear materials by inducing fission from an external pulsed neutron source. In this paper, a detection model based on DDAA analysis technology was established by geant4 Monte Carlo simulation software, and the optimal sensitivity of the detection system is achieved by optimizing different configurations. After the geant4 simulation and optimization, a prototype was established, and experimental research was carried out. The result shows that the prototype can detect 200 g of ²³⁵U in a steel cylinder shield that's of 1.5 cm in inner diameter, 10 cm in thickness and 280 kg in weight.

• Progress in Superconductivity
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• v.14 no.2
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• pp.82-86
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• 2012

A sensitive eight-channel high-Tc Superconducting Interference Device (SQUID) detection system for magnetic contaminant in a lithium ion battery anode was developed. Finding ultra-small metallic foreign matter is an important issue for a manufacturer because metallic contaminants carry the risk of an internal short. When contamination occurs, the manufacturer of the product suffers a great loss from recalling the tainted product. Metallic particles with outer dimensions smaller than 100 microns cannot be detected using a conventional X-ray imaging system. Therefore, a highly sensitive detection system for small foreign matter is required. We have already developed a detection system based on a single-channel SQUID gradiometer and horizontal magnetization. For practical use, the detection width of the system should be increased to at least 65 mm by employing multiple sensors. In this paper, we present an 8-ch high-Tc SQUID roll-to-roll system for inspecting a lithium-ion battery anode with a width of 65 mm. A special microscopic type of a cryostat was developed upon which eight SQUID gradiometers were mounted. As a result, small iron particles of 35 microns on a real lithium-ion battery anode with a width of 70 mm were successfully detected. This system is practical for the detection of contaminants in a lithium ion battery anode sheet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Journal of Korean Society on Water Environment
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• v.40 no.1
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• pp.19-35
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• 2024

The global pandemic, coronavirus disease caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the implementation of wastewater surveillance as a means to monitor the spread of SARS-CoV-2 prevalence in the community. The challenging aspect of establishing wastewater surveillance requires a well-equipped laboratory for wastewater sample analysis. According to previous studies, RT-PCR-based molecular tests are the most widely used and popular detection method worldwide. However, this approach for the detection or quantification of SARS-CoV-2 from wastewater demands a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically takes 6 to 8 hours to provide results for a few samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at regional laboratories. In some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories. The ongoing research and development of alternative and rapid technologies, namely RT-LAMP, ELISA, Biosensors, and GeneXpert, offer a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses. This study aims to discuss the effective regional rapid detection and quantification methods in community wastewater.

• Journal of Radiation Industry
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• v.6 no.2
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• pp.119-123
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• 2012

The detection properties of gamma-irradiated (0~10 kGy) dried seaweed, dried shrimp, and seasoned dried filefish were investigated by photo-stimulated luminescence (PSL) and electron spin resonance (ESR). PSL could be used as a detection method on irradiated dried seaweed and dried shrimp as they showed photon counts greater than 5,000 counts/60 s (positive) in the irradiated samples with doses above l kGy. However, PSL could not be applied to detect irradiated seasoned dried filefish, because gamma-irradiated sample at 10 kGy even yielded photon counts less than 700 counts/60 s (negative). The ESR spectroscopy for only dried shrimp revealed specific signals derived from free radicals captured in the shell of shrimp. As a result, it is considered that PSL or ESR methods for detection of gamma-irradiated dried shrimp and only PSL can be used to detect gamma-irradiated seaweed. Furthermore, it is considered that hydrocarbon analysis of seasoned dried filefish containing fat by GC/MS and Thermo Luminance (TL) analysis of dried seaweed should be studied for detection of irradiation.

• ETRI Journal
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• v.34 no.6
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• pp.892-899
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• 2012

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing-time joint detection framework. This framework detects a wideband M-ary quadrature amplitude modulation (M-QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M-QAM than for binary phase-shift keying or any real modulation scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3751-3770
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• 2015

Many sorts of image processing software facilitate image editing and also generate a great number of doctored images. Forensic technology emerges to detect the unintentional or malicious image operations. Most of forensic methods focus on the detection of single operations. However, a series of operations may be used to sequentially manipulate an image, which makes the operation detection problem complex. Forensic investigators always want to know as much exhaustive information about a suspicious image's entire processing history as possible. The detection of the operation chain, consisting of a series of operations, is a significant and challenging problem in the research field of forensics. In this paper, based on the histogram distribution uniformity of a manipulated image, we propose an operation chain detection scheme to identify histogram equalization (HE) followed by the dither-like operation (DLO). Two histogram features and a local spatial feature are utilized to further determine which DLO may have been applied. Both theoretical analysis and experimental results verify the effectiveness of our proposed scheme for both global and local scenarios.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.99-103
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• 2017

Recently, acccording to price decline and miniaturization of drone, it is increased dramatically that drone usage in various category including military and private sectors. In accordance with popular usage, There is a increasing risk of safety accident, national security and public privacy problem. Hence there is a high demand for study and analysis applicable to the related technology and anti-drone method including drone detection and jamming. In general, it is extremely difficult to detect and recognize drones using conventional sensors. In this paper, we classify drone detection technology and Drone detection experiments are performed using CW RADAR to obtain and analyze micro-doppler pattern. This preliminary study aims to provide fundamental theory on radar drone detection and experimental test results such that in-depth anti-drone technology can be established in future.