An ANN–RTS smoother scheme for accurate INS/GPS integrated attitude determination

Digital mobile mapping, the method that integrates digital imaging with direct geo-referencing, has developed rapidly over the past 15 years. The Kalman filter (KF) is considered an optimal estimation tool for real-time INS/GPS integrated kinematic positioning and orientation determination. However, the accuracy requirements of general mobile mapping applications cannot be easily achieved even when using the KF scheme. Therefore, this study proposes an intelligent scheme combining ANN and RTS backward smoother to overcome the limitations of KF and to enhance the overall accuracy of attitude determination for tactical grade and MEMS INS/GPS integrated systems.

Climate-induced hydrological impacts on the groundwater system of the Pingtung Plain,Taiwan

The Pingtung Plain is one of the most important groundwater-resource areas in southwestern Taiwan. The overexploitation of groundwater in the last two decades has led to serious deterioration in the quantity and quality of groundwater resources in this area. Furthermore, the manifestation of climate change tends to induce the instability of surface-water resources and strengthen the importance of the groundwater resources. Southwestern Taiwan in particular shows decreasing tendencies in both the annual amount of precipitation and annual precipitation days. To effectively manage the groundwater resources of the Pingtung Plain, a numerical modeling approach is adopted to investigate the response of the groundwater system to climate variability. A hydrogeological model is constructed based on the information from geology, hydrogeology, and geochemistry. Applying the linear regression model of precipitation to the next two decades, the modeling result shows that the lowering water level in the proximal fan raises an alarm regarding the decrease of available groundwater in the stress of climate change, and the enlargement of the low-groundwater-level area on the coast signals the deterioration of water quantity and quality in the future. Suitable strategies for water-resource management in response to hydrological impacts of future climatic change are imperative.

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Resumen La llanura Pingtung constituye uno de los recursos de aguas subterráneas más importantes en el Suroeste de Taiwan. La sobreexplotación de las aguas subterráneas en las dos últimas décadas ha dado lugar a un serio deterioro de la calidad y la cantidad de los recursos subterráneos en esta área. En particular el Suroeste de Taiwan muestra una tendencia decreciente en las cantidades de precipitación y en los días anuales de lluvia. Para gestionar de forma efectiva los recursos subterráneos en la Llanura Pingtung, se ha utilizado un modelo numérico aproximado para investigar la respuesta de las aguas subterráneas a la variabilidad climática. Un modelo hidrogeológico se construye a partir de la información geológica, hidrogeológica y geoquímica. Aplicando el modelo de regresión linear de la precipitación para las próximas dos décadas, el modelo resultante muestra que el descenso de los niveles de agua en el abanico proximal es alarmante, observándose el descenso del agua subterránea disponible en la presión del cambio climático, y el crecimiento del área de descensos de niveles de agua subterránea en la costa apunta a un deterioro de la cantidad y calidad del agua subterránea en el futuro. Se imponen pues, estrategias apropiadas para la gestión de los recursos hídricos en respuesta a los impactos hidrológicos del futuro cambio climático.

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Résumé La plaine de Pingtung est l’une des plus importantes zones de ressource en eau souterraine du Sud-Ouest de Taiwan. La surexploitation de l’eau souterraine durant les deux dernières décennies a conduit à une sérieuse détérioration de la quantité et de la qualité des ressources en eau souterraine dans cette zone. De plus, la manifestation des changements climatiques tend à induire une instabilité des ressources en eau souterraine et renforce l’importance des ressources en eau souterraine. Le Sud-Ouest de Taiwan montre, en particulier, des précipitations annuelles et des nombres annuels de jour de pluie à la baisse. Pour gérer efficacement les ressources en eau souterraine de la Plaine de Pingtung, une approche par modélisation numérique est adoptée pour étudier la réponse du système hydrogéologique aux variabilités climatiques. La construction du modèle hydrogéologique est basée sur les informations géologiques, hydrogéologiques et géochimiques. En appliquant le modèle de régression linéaire aux précipitations pour les deux prochaines décades, le résultat de la modélisation montre que la baisse du niveau d’eau atteint un état alarmant au regard de la décroissance des eaux souterraines disponibles et la contrainte du changement climatique, tandis que l’extension de la zone de niveau bas des eaux souterraines à la cote indique une détérioration de la quantité et de la qualité de l’eau dans le futur. Des stratégies convenables pour la gestion des ressources en eau souterraine en réponse aux impacts hydrologiques de futurs changements climatiques sont impératifs.

     

Improvement of earthquake locations with the Marine Cable Hosted Observatory (MACHO) offshore NE Taiwan

In order to improve the locating capability for offshore earthquakes and tsunamis monitored off northeastern Taiwan, a cable-based ocean bottom seismographic observatory named “Marine Cable Hosted Observatory” (MACHO) was constructed and began operation at the end of 2011. The installed instruments of the observatory include a broadband seismometer, a strong-motion seismometer and a pressure gauge. In addition, various scientific instruments could be deployed for other purposes as well. At present, the seismic data are transmitted in real-time via a fiber cable, and integrated into the current inland seismographic network in Taiwan. The ocean bottom station has contributed to provide high quality seismic data already. According to observations from January 2012 to June 2013, there were a total of 15,168 earthquakes recorded by the system. By using the data from the ocean bottom station, the number of relocated earthquakes with an azimuth gap less than 180 degrees substantially increase about 34 %. Meanwhile, the root–mean–square of the time residual, the error in epicenter, and the error in depth of the earthquake locations decrease. Therefore, the implementation of MACHO has the advantage of extending the coverage of existing the Taiwan seismic network to the offshore, providing more accurate and real-time seismic data for offshore earthquakes monitoring. The results show that MACHO is crucial and necessary for monitoring seismic activities in northeastern Taiwan.     

Multisensor integration using neuron computing for land-vehicle navigation

Most of the present navigation sensor integration techniques are based on Kalman-filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multisensor integration, it still has some drawbacks in terms of stability, computation load, immunity to noise effects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of artificial neural network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multisensor integration approach for fusing data from an inertial navigation system (INS) and differential global positioning system (DGPS) hardware utilizing multilayer feed-forward neural networks with a back propagation learning algorithm. In addition, it addresses the impact of neural network (NN) parameters and random noise on positioning accuracy. Electronic Publication     

An experimental study of mitigating coastal sand dune erosion by microbial- and enzymatic-induced carbonate precipitation

Acta Geotechnica - Due to more extreme weather events and accelerating sea-level rise, coastal sand dunes are subjected to more frequent storm wave inundation and surge impacts, which contribute to...     

山东南部海滩沉积物重金属污染评价及磁学诊断

海滩作为海陆交汇和人类活动最为频繁的区域之一, 由于排污、附近工厂和垃圾倾倒等造成的重金属污染问题亟须重视。以山东南部日照市海滨国家森林公园、芙蓉村、涛雒镇三个海滩为例, 对其表层与垂向沉积物中的Cr、Ni、V、Fe、Zn、Co、Pb共7种重金属含量进行测试, 并使用污染负荷指数(pollution load index, PLI)法与地质累计指数(I_geo)法进行评价。同时对样品进行环境磁学参数测试并建立海滩沉积物重金属污染的磁学诊断模型。PLI显示, 三个海滩均处于污染状态, 污染程度为森林公园海滩＜芙蓉村海滩＜涛雒镇海滩。I_geo结果表明, 森林公园海滩存在V污染, 污染样品占比18%; 芙蓉村海滩存在Ni和V污染, 污染样品占比约33%; 涛雒镇海滩存在Cr、Ni、V污染, 污染样品占比约38%。研究区海滩重金属污染产生的主要原因为当地的工业生产以及水产养殖废水排放。磁参数归一化多元线性分析显示, 质量磁化率χ、饱和等温剩磁SIRM、非磁滞剩磁磁化率χ_ARM三个参数和重金属元素含量关系密切, 可以较好指示重金属含量和污染程度。质量磁化率诊断结果表明,当日照市海滩0.063~0.125 mm粒级沉积物磁化率值大于1 000×10^-8 m³/kg时, 需要注意海滩可能存在重金属元素污染, 可进行更深一步的污染评估。     

Structural properties of effective potential model by liquid state theories

This paper investigates the structural properties of a model fluid dictated by an effective inter-particle oscillatory potential by grand canonical ensemble Monte Carlo (GCEMC) simulation and classical liquid state theories. The chosen oscillatory potential incorporates basic interaction terms used in modeling of various complex fluids which is composed of mesoscopic particles dispersed in a solvent bath, the studied structural properties include radial distribution function in bulk and inhomogeneous density distribution profile due to influence of several external fields. The GCEMC results are employed to test the validity of two recently proposed theoretical approaches in the field of atomic fluids. One is an Ornstein--Zernike integral equation theory approach; the other is a third order + second order perturbation density functional theory. Satisfactory agreement between the GCEMC simulation and the pure theories fully indicates the ready adaptability of the atomic fluid theories to effective model potentials in complex fluids, and classifies the proposed theoretical approaches as convenient tools for the investigation of complex fluids under the single component macro-fluid approximation.     

An intelligent and autonomous MEMS IMU/GPS integration scheme for low cost land navigation applications

An intelligent scheme to integrate inertial navigation system/global positioning system (GPS) is proposed using a constructive neural network (CNN) to overcome the limitations of current schemes, namely Kalman filtering (KF). The proposed CNN technique does not require prior knowledge or empirical trials to implement the proposed architecture since it is able to construct its architecture “on the fly,” based on the complexity of the vehicle dynamic variations. The proposed scheme is implemented and tested using Micro-electro-mechanical systems inertial measurement unit data collected in a land-vehicle environment. The performance of the proposed scheme is then compared with the multi-layer feed-forward neural networks (MFNN) and KF- based schemes in terms of positioning accuracy during GPS signal outages. The results are then analyzed and discussed in terms of positioning accuracy and learning time. The preliminary results presented in this article indicate that the positioning accuracy were improved by more than 55% when the MFNN and CNN-based schemes were implemented. In addition, the proposed CNN was able to construct the topology by itself autonomously on the fly and achieve similar prediction performance with less hidden neurons compared to MFNN-based schemes.     

基于高质量发展视角的中国碳配额省级分配方案评价

碳配额分配是碳排放控制的关键,在实现国家“双碳”目标下探寻更利于高质量发展的省级分配方案具有重要意义。基于平等主义、历史责任、支付能力、排放效率和可行性分配准则,使用综合指标法设计了10种分配方案,并以高质量发展为标准,使用Malmquist指数模型对这些方案进行评价。结果表明：我国各省份在不同分配方案下拥有多样化的碳配额,多数地区根据排放效率准则、支付能力准则、历史责任准则和可行性准则（方案F）获得最大配额。从国家层面看,方案F具有最佳的经济高质量发展性能,是相对最优的选择。从区域层面看,各省在不同分配方案下有不同的经济高质量发展表现,多数地区根据平等主义准则、排放效率准则和可行性准则（方案G）实现最优发展。排放效率指标的选取对分配方案的结果具有稳健性。