PIXE study on recovery of making-technology of Chinese Longquan celadon made in the Southern Song Dynasty (1127–1279 CE)

Proton induced X-ray emission (PIXE) technique and factor analysis were used to study the recovery of making-technology of Chinese Longquan celadon made in the late Southern Song Dynasty (1127–1279 CE). Based on factor analysis, for the raw material for body and the recipe for glaze, there is a close but slightly different relationship between these Longquan celadons unearthed from the core area of Dayao and the non-core area, despite all the Longquan celadons belong to the high-calcium-glaze system. The chemical compositions of most of the tentative body and glaze samples are very close to those of Longquan celadons unearthed from the core area of Dayao and the non-core area. This study can provide a guidance for recovery of making-technology of Longquan celandon.     

Strength of pure alumina ceramics above 1 GPa

Up to now, commercially available alumina ceramics were claimed to have strength between 400 and 550 MPa. However, our study shows strength ~ 2 times higher for commercially available alumina than commonly believed. The average and characteristic strength, measured on 31 pure alumina ceramic discs by ball on three balls (B3B) test, were 1205 ± 93 MPa and 1257 MPa, respectively, with a Weibull modulus of m = 11.8. Tested specimens were in form of discs with a diameter of 5 mm and thickness 0.5 mm. The grain size distribution of the alumina is bimodal with an average grain size of ~ 850 nm measured at the surface. The fracture reveals a mixed transgranular / intergranular failure mode. To avoid incorporation of additional flaws, the discs were tested as sintered. The characteristic flexural strength measured in B3B was recalculated according to Weibull theory for standard 4-point bending bars of size 3 × 4 × 45 mm as bend 856 MPa. The measured strength of nearly 900 MPa shows the potential of strength for high purity alumina ceramics.     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Enhanced relative permittivity in niobium and europium co-doped TiO2 ceramics

The appearance of colossal permittivity materials broadened the choice of materials for energy-storage applications. In this work, colossal permittivity in ceramics of TiO₂ co-doped with niobium and europium ions ((Eu_0.5Nb_0.5)_xTi_1-xO₂ ceramics) was reported. A large permittivity (ε_r ～ 2.01?×?10⁵) and a low dielectric loss (tanδ ～ 0.095) were observed for (Eu_0.5Nb_0.5)_xTi_1-xO₂ (x?=?1%) ceramics at 1?kHz. Moreover, two significant relaxations were observed in the temperature dependence of dielectric properties for (Eu, Nb) co-doped TiO₂ ceramics, which originated from defect dipoles and electron hopping, respectively. The low dielectric loss and high relative permittivity were ascribed to the electron-pinned defect-dipoles and electrons hopping. The (Eu_0.5Nb_0.5)_xTi_1-xO₂ ceramic with great colossal permittivity is one of the most promising candidates for high-energy density storage applications.     

Back-analysis of geotechnical parameters on PVD-improved ground in the Mekong Delta

This study presents a back-analysis of geotechnical parameters on prefabricated vertical drain improved ground at a site in the Mekong Delta. Various time?settlement behaviors that reflected different clay thicknesses and loading patterns were observed. The total surface settlement behavior at several monitoring locations was simulated using an updated exponential method that considered staged construction. The analyzed results were validated by substituting the values into a theoretical solution for radial consolidation. The estimated theoretical behaviors were comparable with the monitored behaviors. The geotechnical parameters were back-analyzed by applying the previously analyzed results to various theoretical and empirical formulas. However, the use of extensometer data that were installed at large intervals produced different values of the geotechnical properties. Furthermore, finite element analysis supported the back-analyzed total settlement behaviors and nearly disregarded the application of the geotechnical properties that were obtained using either surface or subsurface settlement data. However, settlements and excess pore pressures in the sublayers were not successfully predicted even when the geotechnical properties were adjusted. Thus, subsurface instruments that can be installed closely in thick clay deposits are required to reliably reevaluate the variations in geotechnical properties along a certain depth.     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.     

智能化救援监控系统在煤矿安全生产中的应用

针对煤矿发生事故后传统救援监控系统无法实时对井下人员进行动态定位，导致矿井救援盲目性大、救援效率差、救援难度大等技术难题，为了进一步提高煤矿救援效率，通过技术研究，设计了一套以通信基站为核心的智能化救援监控系统，分析了该系统结构组成、工作原理，通过在担水沟煤矿井下实际应用效果来看，智能化救援监控系统对人员定位精准度达95%，实现人员动态位置三维成像，救援效率提高至80%以上，有效缩短了煤矿事故救援时间，取得了显著应用成效。     

Design of Feedback Shift Register of Against Power Analysis Attack

Stream ciphers based on linear feedback shift register (LFSR) are suitable for constrained environments, such as satellite communications, radio frequency identification devices tag, sensor networks and Internet of Things, due to its simple hardware structures, high speed encryption and lower power consumption. LFSR, as a cryptographic primitive, has been used to generate a maximum period sequence. Because the switching of the status bits is regular, the power consumption of the LFSR is correlated in a linear way. As a result, the power consumption characteristics of stream cipher based on LFSR are vulnerable to leaking initialization vectors under the power attacks. In this paper, a new design of LFSR against power attacks is proposed. The power consumption characteristics of LFSR can be masked by using an additional LFSR and confused by adding a new filter Boolean function and a flip-flop. The design method has been implemented easily by circuits in this new design in comparison with the others.