A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image features.There are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient records.Several methods are applied to enhance the retrieval performance of CBMIR systems.Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems.This study proposes the relative difference-based similarity measure(RDBSM)for CBMIR.The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features.Furthermore,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)models.Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval measures.The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

Exploration of French-Chinese Translation Methods of Electrical Engineering Terminology Using Online Image-Text Retrieval Mode

With the incessant propulsion of the Open Door Policy,which is related to the consolidation of international collaborative partnerships,an increasing number of Chinese companies are moving toward cooperating countries to participate in infrastructure construction,employing a win-win strategy in favor of the people and governments of both countries.Among the cooperation domains,our country’s electrical companies have achieved a series of remarkable results in the international Engineering,Procurement,and Construction(EPC)project market with their outstanding business capabilities and technical advantages.Nevertheless,some shortcomings cannot be overlooked,the most notable of which appears to be the impediment associated with engineering translation,which has always been an obsession among translators of Chinese companies.Taking the transmission line project in the Republic of Madagascar as an example,an analysis of French-Chinese translation methods of electrical engineering terminology in the field of the transmission line is carried out.

Travel time and alternative configurations analysis for automated storage/retrieval systems based on geometrical method

In order to evaluate the efficiency of the automated storage/retrieval system(AS/RS)accurately,and compare different layouts of the AS/RS using mean travel time,under randomized storage conditions,an exact,geometry-based analytical model is presented.The model can be used to compute the expected single-command and dual-command travel time for a storage/retrieval(S/R)machine which can travel simultaneously horizontally and vertically as it moves along a storage aisle.The rack may be either square in time or non square in time.Additionally,the alternative layouts of the AS/RS and travel-time models are examined.Comparing with setting the I/O point at the left-lower corner of the rack,setting the I/O point at any point at the vertical edge can help enhance the efficiency of the AS/RS.

Study of the Simultaneous Physical Retrieval Method for Meteorological Parameters over the Continental Plateau of China

It is common knowledge that continental retrieval especially for Qinghai-Xizang Plateau has not been solved todate. In order to explore applicable inverse model and method for continent including the plateau, in this study authors use an improved simultaneous physical retrieval method hereafter referred to as the ISPRM, for computing meteorological parameters from NOAA-10 satellite TOVS data. The retrieval results verified by nearby radiosondesshow that the ISPRM is more applicable for the continental plateau.

Application of the Tikhonov regularization method to wind retrieval from scatterometer data I.Sensitivity analysis and simulation experiments

Scatterometer is an instrument which provides all-day and large-scale wind field information, and its application especially to wind retrieval always attracts meteorologists. Certain reasons cause large direction error, so it is important to find where the error mainly comes. Does it mainly result from the background field, the normalized radar cross-section （NRCS） or the method of wind retrieval？ It is valuable to research. First, depending on SDP2.0, the simulated ＇true＇ NRCS is calculated from the simulated ＇true＇ wind through the geophysical mode] function NSCAT2. The simulated background field is configured by adding a noise to the simulated ＇true＇ wind with the non-divergence constraint. Also, the simulated ＇measured＇ NRCS is formed by adding a noise to the simulated ＇true＇ NRCS. Then, the sensitivity experiments are taken, and the new method of regularization is used to improve the ambiguity removal with simulation experiments. The results show that the accuracy of wind retrieval is more sensitive to the noise in the background than in the measured NRCS; compared with the two-dimensional variational （2DVAR） ambiguity removal method, the accuracy of wind retrieval can be improved with the new method of Tikhonov regularization through choosing an appropriate regularization parameter, especially for the case of large error in the background. The work will provide important information and a new method for the wind retrieval with real data.

Probabilistic 2D Shape Retrieval and Applications via the Method of Hurwitz-Radon Matrices

Artificial intelligence and computer vision need methods for 2D （two-dimensional） shape retrieval having discrete set of boundary points. A novel method of MHR （Hurwitz-Radon Matrices） is used in shape modeling. Proposed method is based on the family of MHR which possess columns composed of orthogonal vectors. 2D curve is retrieved via different functions as probability distribution functions： sine, cosine, tangent, logarithm, exponent, arcsin, arccos, arctan and power function. Created from the family of N-1 MHR and completed with the identical matrix, system of matrices is orthogonal only for dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very significant for stability and high precision of calculations. MHR method is interpolating the function point by point without using any formula of function. Main features of MHR method are： accuracy of curve reconstruction depending on number of nodes and method of choosing nodes, interpolation of L points of the curve is connected with the computational cost of rank O（L）, MHR interpolation is not a linear interpolation.

High Efficient Methods of Content-based 3D Model Retrieval

Content-based 3D model retrieval is of great help to facilitate the reuse of existing designs and to inspire designers during conceptual design. However, there is still a gap to apply it in industry due to the low time efficiency. This paper presents two new methods with high efficiency to build a Content-based 3D model retrieval system. First, an improvement is made on the ＂Shape Distribution （D2）＂ algorithm, and a new algorithm named ＂Quick D2＂ is proposed. Four sample 3D mechanical models are used in an experiment to compare the time cost of the two algorithms. The result indicates that the time cost of Quick D2 is much lower than that of D2, while the descriptors extracted by the two algorithms are almost the same. Second, an expandable 3D model repository index method with high performance, namely, RBK index, is presented. On the basis of RBK index, the search space is pruned effectively during the search process, leading to a speed up of the whole system. The factors that influence the values of the key parameters of RBK index are discussed and an experimental method to find the optimal values of the key parameters is given. Finally, ＂3D Searcher＂, a content-based 3D model retrieval system is developed. By using the methods proposed, the time cost for the system to respond one query online is reduced by 75% on average. The system has been implemented in a manufacturing enterprise, and practical query examples during a case of the automobile rear axle design are also shown. The research method presented shows a new research perspective and can effectively improve the content-based 3D model retrieval efficiency.

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS)from HH-polarized Sentinel-1(S1)SAR images.The Polarization Ratio(PR)models combined with the CMOD5.N Geophysical Model Function(GMF)is used for SSWS retrieval from the HH-polarized SAR data.We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data.The recently proposed CMODH,i.e.,retrieving SSWS directly from the HHpolarized S1 data is also validated.The results indicate that the CMODH model performs better than results achieved using the PR models.We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data.The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods.Compared to the buoy measurements,the bias,root mean square error(RMSE)and scatter index(SI)of wind speed retrieved by the BP neural network model are 0.10 m/s,1.38 m/s and 19.85%,respectively,while compared to the ASCAT dataset the three parameters of training set are–0.01 m/s,1.33 m/s and 15.10%,respectively.It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

Retrieval and Regional Distribution Analysis of Ammonia,Sulfur Dioxide and Nitrogen Dioxide in the Urban Environment Using Ultraviolet DOAS Algorithm

Aiming at the in situ and mobile observation of urban environmental air pollution,a portable instrument using ultraviolet spectrum retrieval algorithm was developed based on the basis of Differential Optical Absorption Spectroscopy(DOAS)and multiple-pass cell technique.Typical trace gas pollutants,NH3,SO2,and NO2,were explored using their optical spectral characteristics in deep ultraviolet wavelength range from 210 to 215 nm.The gas concentration was retrieved by Lambert-Beer’s law and nonlinear least square method.With an optimized optical alignment,the detection limits of NH3,SO2,NO2 were estimated to be 2.2,2.3,and 36.2 ppb,respectively.The system was used in carrying out some cruise observations in Chengdu,China.During the entire period,the polluted gases showed varied distribution and typical daily average concentrations ofNH3,SO2,NO2 were 23.2,3.5,and 106.0 ppb,respectively.The contributions from different sources were analyzed combined with the HYSPLIT model.Results show that the portable DOAS system is a convenient and effective tool for regional distribution measurement and pollution source monitoring.

Precipitation Retrieval from Himawari-8 Satellite Infrared Data Based on Dictionary Learning Method and Regular Term Constraint

In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.

An Improved Method for Doppler Wind and Thermo dynamic Retrievals

A variational method is developed to retrieve winds in the first step and then thermodynamic fields in the second step from Doppler radar observations. In the first step, wind fields are retrieved at two time levels: the beginning and ending times of the data assimilation period, simultaneously from two successive volume scans by using the weak form constraints provided by the mass continuity and vorticity equations. As the retrieved wind fields are expressed by Legendre polynomial expansions at the beginning and ending times, the time tendency term in the vorticity equation can be conveniently formulated, and the retrieved winds can be compared with the radar observed radial winds in the cost function at the precise time and position of each radar beam. In the second step, the perturbation pressure and temperature fields at the middle time are then derived from the retrieved wind fields and the velocity time tendency by using the weak form constraints provided by the three momentum equations. The merits of the new method are demonstrated by numerical experiments with simulated radar observations and compared with the traditional least squares methods which consider neither the precise observation times and positions nor the velocity time tendency. The new method is also applied to real radar data for a heavy rainfall event during the 2001 Meiyu season in China.

An improved wind retrieval algorithm for the HY-2A scatterometer

Since January 2012,the National Satellite Ocean Application Service has released operational wind products from the HY-2A scatterometer(HY2-SCAT),using the maximum-likelihood estimation(MLE) method with a median filter. However,the quality of the winds retrieved from HY2-SCAT depends on the sub-satellite cross-track location,and poor azimuth separation in the nadir region causes particularly low-quality wind products in this region. However,an improved scheme,i.e.,a multiple solution scheme(MSS) with a two-dimensional variational analysis method(2DVAR),has been proposed by the Royal Netherlands Meteorological Institute to overcome such problems. The present study used the MSS in combination with a 2DVAR technique to retrieve wind data from HY2-SCAT observations. The parameter of the empirical probability function,used to indicate the probability of each ambiguous solution being the "true" wind,was estimated based on HY2-SCAT data,and the 2DVAR method used to remove ambiguity in the wind direction. A comparison between MSS and ECMWF winds showed larger deviations at both low wind speeds(below 4 m/s) and high wind speeds(above 17 m/s),whereas the wind direction exhibited lower bias and good stability,even at high wind speeds greater than 24 m/s. The two HY2-SCAT wind data sets,retrieved by the standard MLE and the MSS procedures were compared with buoy observations. The RMS error of wind speed and direction were 1.3 m/s and 17.4°,and 1.3 m/s and 24.0° for the MSS and MLE wind data,respectively,indicating that MSS wind data had better agreement with the buoy data. Furthermore,the distributions of wind fields for a case study of typhoon Soulik were compared,which showed that MSS winds were spatially more consistent and meteorologically better balanced than MLE winds.

EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.

^(41)Ca的AMS测量方法及在生物医学领域的应用

钙(Ca)的长寿命放射性核素^(41)Ca是理想且安全的生物示踪剂,在生物医学领域具有重要价值,但其发展一直受制于测量技术。传统衰变计数法测量效率极低、样品需求量高且耗费时间长,无法有效开展^(41)Ca分析;普通质谱方法很难剔除同量异位素41K和分子碎片的干扰,难以获得理想的探测限。而加速器质谱法(AMS)测定^(41)Ca具有样品用量小(mg量级)、测量时间短(约1 h)、检测灵敏度高(^(41)Ca/^(40)Ca丰度灵敏度最低可至10^(-15))等优点,是目前分析^(41)Ca最常用的方法。AMS测量的关键是如何有效剔除或分离同量异位素41K。本研究从样品形式的选择和仪器技术两方面综述了^(41)Ca的AMS测量方法发展进程。一方面,使用CaH_(2)或CaF_(2)作为主要样品形式目前可以有效抑制41K的干扰。CaF_(2)制备流程更简单,存储更方便,束流引出阶段对同量异位素41K的抑制在4个量级左右,满足添加^(41)Ca为示踪剂的生物医学样品的分析要求。另一方面,随着AMS逐渐小型化,^(41)Ca-AMS测量技术也在不断改进。对比不同能量下AMS分析^(41)Ca的特点、优势和局限性,其中:高能量大型(≥5 MV)AMS对41K有足够的辨别能力,测量灵敏度一般在10^(-14)~10^(-15),但由于成本和仪器运行专业性过高的要求,使其应用不够广泛;低能量小型(≤3 MV)AMS应用最为广泛,虽然只能将^(41)Ca和41K部分分离,但可以通过dE/dx、ΔTOF和ISA等方法对^(41)Ca实现直接测量,测量灵敏度在10^(-12)~10^(-13)或有望更低;而更低能量的紧凑型(≤1 MV)AMS无法在低能量情况下将^(41)Ca和41K分离,只能通过39K校正的间接方法实现对^(41)Ca的测量,测量灵敏度一般为10^(-11)~10^(-12)之间。在此基础上,本研究介绍了基于^(41)Ca-AMS分析技术在生物医学领域中的应用。^(41)Ca示踪技术是目前探究生物体骨钙代谢的重要手段,在评估人类骨骼健康、监测骨重塑动态和建立人类钙平衡的线性动力学模型等方面具有潜力。^(41)Ca-AMS示踪技术可以用于临床医学中相关疾病的早期诊断和预后监测;评估失重对股骨摄取外源钙能力的影响以及骨量下降和骨钙代谢紊乱的机理,从而提出针对性的预防和治疗措施;^(41)Ca标记体内钙库,可以更精准的测量钙吸收率,为临床上补钙剂量的确定提供理论指导;还可以通过探究细胞内钙离子动力学从而研究相关疾病的机理。此外,^(41)Ca-AMS技术还可以对植物体进行示踪研究,有望为农作物的科学补钙和破译植物-土壤系统界面化学相互作用过程提供帮助。可见,^(41)Ca在生物医学领域研究中具有广泛的应用,且由于其极低的放射性,在涉及人体的研究中其安全性有着不可替代的优势,而近年随着国内^(41)Ca-AMS分析技术的发展,有望为我国深入开展相关前沿研究提供技术支撑。

A Three-Dimensional Satellite Retrieval Method for Atmospheric Temperature and Moisture Profiles

A three-dimensional variational method is proposed to simultaneously retrieve the 3-D atmospheric temperature and moisture profiles from satellite radiance measurements. To include both vertical structure and the horizontal patterns of the atmospheric temperature and moisture, an EOF technique is used to decompose the temperature and moisture field in a 3-D space. A number of numerical simulations are conducted and they demonstrate that the 3-D method is less sensitive to the observation errors compared to the 1-D method. When the observation error is more than 2.0 K, to get the best results, the truncation number for the EOF＇s expansion have to be restricted to 2 in the 1-D method, while it can be set as large as 40 in a 3-D method. This results in the truncation error being reduced and the retrieval accuracy being improved in the 3-D method. Compared to the 1-D method, the rms errors of the 3-D method are reduced by 48% and 36% for the temperature and moisture retrievals, respectively. Using the real satellite measured brightness temperatures at 0557 UTC 31 July 2002, the temperature and moisture profiles are retrieved over a region （20°-45°N, 100°- 125°E） and compared with 37 collocated radiosonde observations. The results show that the retrieval accuracy with a 3-D method is significantly higher than those with the 1-D method.

基于权值的新能源集控电量均衡CSMA/CA改进算法

针对新能源集控任务下发过程中,单个节点电量消耗较大,直接影响电量均衡的问题,提出一种基于权值的新能源集控电量均衡CSMA/CA改进算法。建立电池等效模型,通过计算健康系数推导新能源集控节点当前电量。计算各个电池当前电量与预期之间的差值,当60%以上节点的差值大于设定的阈值,认为电量存在不均衡的问题。利用熵权法与CSMA/CA算法计算三个关键变量的权值,即NB、CW和BE,并赋权,实现CSMA/CA算法改进,以此动态调控任务,实现新能源集控电量均衡消耗。结果表明:利用权值改进后,电量均衡度平均值明显更高。这种方法为促进电量均衡提供新思路。

Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images.Thus,there were lots of efforts trying to automate the classification operation and retrieve similar images accurately.To reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically.Then,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network.The Siamese model built and trained at first from scratch but,it didn’t generated high evaluation metrices.Thus,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices.Afterward,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved images.Reaching that the highest evaluation parameters generated using the Cosine distance metric.Moreover,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time efficiently.After extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

L1/2 -Regularized Quantile Method for Sparse Phase Retrieval

The sparse phase retrieval aims to recover the sparse signal from quadratic measurements. However, the measurements are often affected by outliers and asymmetric distribution noise. This paper introduces a novel method that combines the quantile regression and the L1/2-regularizer. It is a non-convex, non-smooth, non-Lipschitz optimization problem. We propose an efficient algorithm based on the Alternating Direction Methods of Multiplier (ADMM) to solve the corresponding optimization problem. Numerous numerical experiments show that this method can recover sparse signals with fewer measurements and is robust to dense bounded noise and Laplace noise.