ALBERT

Description: This paper presents a systematic pioneering study on the use of agricultural-purpose frequency domain reflectometry (FDR) sensors to monitor temperature and moisture of a subgrade in highway extension and reconstruction engineering. The principle of agricultural-purpose FDR sensors and the process for embedding this kind of sensors for subgrade engineering purposes are introduced. Based on field measured weather data, a numerical analysis model for temperature and moisture content in the subgrade’s soil is built. Comparisons of the temperature and moisture data obtained from numerical simulation and FDR-based measurements are conducted. The results show that: (1) the embedding method and process, data acquisition, and remote transmission presented are reasonable; (2) the temperature and moisture changes are coordinated with the atmospheric environment and they are also in close agreement with numerical calculations; (3) the change laws of both are consistent at positions where the subgrade is compacted uniformly. These results suggest that the data measured by the agricultural-purpose FDR sensors are reliable. The findings of this paper enable a new and effective real-time monitoring method for a subgrade’s temperature and moisture changes, and thus broaden the application of agricultural-purpose FDR sensors.

Description: In this contribution, a new surface acoustic wave (SAW)-based sensor was proposed for sensing hydrogen sulfide (H2S) at room temperature (30 °C), which was composed of a phase discrimination circuit, a SAW-sensing device patterned with delay line, and a triethanolamine (TEA) coating along the SAW propagation path of the sensing device. The TEA was chosen as the sensitive interface for H2S sensing, owing to the high adsorption efficiency by van der Waals’ interactions and hydrogen bonds with H2S molecules at room temperature. The adsorption in TEA towards H2S modulates the SAW propagation, and the change in the corresponding phase was converted into voltage signal proportional to H2S concentration was collected as the sensor signal. A SAW delay line patterned on Y-cut quartz substrate with Al metallization was developed photographically, and lower insertion and excellent temperature stability were achieved thanks to the single-phase unidirectional transducers (SPUDTs) and lower cross-sensitivity of the piezoelectric substrate. The synthesized TEA by the reaction of ethylene oxide and ammonia was dropped into the SAW propagation path of the developed SAW device to build the H2S sensor. The developed SAW sensor was characterized by being collecting into the phase discrimination circuit. The gas experimental results appear that fast response (7 s at 4 ppm H2S), high sensitivity (0.152 mV/ppm) and lower detection limit (0.15 ppm) were achieved at room temperature. It means the proposed SAW sensor will be promising for H2S sensing.

Description: Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

Description: Through-wall-radar imaging is of interest for mapping the wall layout of buildings and for the detection of stationary targets within buildings. In this paper, we present an easy single-side two-location spotlight imaging method for both wall layout mapping and stationary target detection by utilizing multiple-input multiple-output (MIMO) through-wall-radar. Rather than imaging for building walls directly, the images of all building corners are generated to speculate wall layout indirectly by successively deploying the MIMO through-wall-radar at two appropriate locations on only one side of the building and then carrying out spotlight imaging with two different squint-views. In addition to the ease of implementation, the single-side two-location squint-view detection also has two other advantages for stationary target imaging. The first one is the fewer multi-path ghosts, and the second one is the smaller region of side-lobe interferences from the corner images in comparison to the wall images. Based on Computer Simulation Technology (CST) electromagnetic simulation software, we provide multiple sets of validation results where multiple binary panorama images with clear images of all corners and stationary targets are obtained by combining two single-location images with the use of incoherent additive fusion and two-dimensional cell-averaging constant-false-alarm-rate (2D CA-CFAR) detection.

Description: Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

Description: Sensors, Vol. 18, Pages 3158: A Disposable and Multi-Chamber Film-Based PCR Chip for Detection of Foodborne Pathogen Sensors doi: 10.3390/s18093158 Authors: Nam Ho Bae Sun Young Lim Younseong Song Soon Woo Jeong Seol Yi Shin Yong Tae Kim Tae Jae Lee Kyoung G. Lee Seok Jae Lee Yong-Jun Oh Yoo Min Park Since the increment of the threat to public health caused by foodborne pathogens, researches have been widely studied on developing the miniaturized detection system for the on-site pathogen detection. In the study, we focused on the development of portable, robust, and disposable film-based polymerase chain reaction (PCR) chip containing a multiplex chamber for simultaneous gene amplification. In order to simply fabricate and operate a film-based PCR chip, different kinds of PCR chambers were designed and fabricated using polyethylene terephthalate (PET) and polyvinyl chloride (PVC) adhesive film, in comparison with commercial PCR, which employs a stereotyped system at a bench-top scale. No reagent leakage was confirmed during the PCR thermal cycling using the film PCR chip, which indicates that the film PCR chip is structurally stable for rapid heat cycling for DNA amplification. Owing to use of the thin film to fabricate the PCR chip, we are able to realize fast thermal transfer from the heat block that leads to short PCR amplification time. Moreover, using the film PCR chip, we could even amplify the target pathogen with 10 CFU mL−1. The artificially infected milk with various concentration of Bacillus cereus was successfully amplified on a single film PCR chip. On the basis of the reliable results, the developed film PCR chip could be a useful tool as a POCT device to detect foodborne pathogens via genetic analysis.

Description: A vibration isolator embedded in precision equipment, such as a scanning electron microscope (SEM), wafer inspection equipment, and nanoimprint lithography equipment, play a critical role in achieving the maximum performance of the equipment during the fabrication of nano/micro-electro-mechanical systems. In this study, the factors that degrade the performance of SEM equipment with isolation devices are classified and discussed, and improvement measures are proposed from the viewpoints of the measured image patterns and vibrations in comparison with the relevant vibration criteria. In particular, this study quantifies the image patterns measured using SEMs, and the results are discussed along with the measured vibration. A guide for the selection of mounting equipment is presented by performing vibration analysis on the lower mount of the dual elastic mount configuration applied to the SEM, as well as the image patterns analyzed with that configuration. In addition, design modifications for the mount and its arrangement are suggested based on impact tests and numerical simulations.

Description: A Love wave-based sensing chip incorporating a supramolecular cryptophane A (CrypA) thin film was proposed for methane gas sensing in this work. The waveguide effect in the structure of SiO2/36° YX LiTaO3 will confine the acoustic wave energy in SiO2 thin-film, which contributes well to improvement of the mass loading sensitivity. The CrypA synthesized from vanillyl alcohol by a double trimerisation method was dropped onto the wave propagation path of the sensing device, and the adsorption to methane gas molecules by supramolecular interactions in CrypA modulates the acoustic wave propagation, and the corresponding frequency shifts were connected as the sensing signal. A theoretical analysis was performed to extract the coupling of modes for sensing devices simulation. Also, the temperature self-compensation of the Love wave devices was also achieved by using reverse polarity of the temperature coefficient in each media in the waveguide structure. The developed CrypA coated Love wave sensing device was connected into the differential oscillation loop, and the corresponding gas sensitive characterization was investigated. High sensitivity, fast response, and excellent temperature stability were successfully achieved.