Evaluation of Correction Algorithm for the Reflectance Measured with Optical Glass

The proposed algorithm for reflectance measured with optical glass has been verified with materials of various forms. The reflectances measured with optical glass ( raw), without glass (true) and corrected by the algorithm are compared. The results show that the corrected reflectance agrees very well with true one and their color differences fall below the acceptable limit, which indicates the validity of the correction algorithm. The algorithm could be used not only for fiber-forming materials, but also for powder-forming, granulate-forming, etc.

Coordinate correction algorithm for WLAN indoor positioning

In the fingerprint matching-based wireless local area network(WLAN) indoor positioning system,Kalman filter(KF) is usually applied after fingerprint matching algorithms to make positioning results more accurate and consecutive.But this method,like most methods in WLAN indoor positioning field,fails to consider and make use of users' moving speed information.In order to make the positioning results more accurate through using the users' moving speed information,a coordinate correction algorithm(CCA) is proposed in this paper.It predicts a reasonable range for positioning coordinates by using the moving speed information.If the real positioning coordinates are not in the predicted range,it means that the positioning coordinates are not reasonable to a moving user in indoor environment,so the proposed CCA is used to correct this kind of positioning coordinates.The simulation results prove that the positioning results by the CCA are more accurate than those calculated by the KF and the CCA is effective to improve the positioning performance.

A new adaptive nonuniformity correction algorithm for infrared line scanner based on neural networks

The striping pattern nonuniformity of the infrared line scanner （IRLS） severely limits the system performance. An adaptive nonuniformity correction （NUC） algorithm for IRLS using neural network is proposed. It uses a one-dimensional median filter to generate ideal output of network and can complete NUC by a single frame with a high correction level. Applications to both simulated and real infrared images show that the algorithm can obtain a satisfactory result with low complexity, no need of scene diversity or global motion between consecutive frames. It has the potential to realize real-time hardware-based applications.

On the Generation Algorithm of VRS Virtual Observations

In the past few years, network RTK positioning technology, especially the VRS technology, has been widely used in some parts of China and in many countries around the world. The principle of the VRS technology is discussed with corresponding formula deduction, and detailed descriptions and applications of VRS corrections and virtual observations generation algorithm are given.

A Novel Method for Wet Gas Flow Measurements Based on an Over-Reading Principle

A novel method to measure the flow rate in a wet gas is presented.Due to the presence of liquid,there is a deviation in the measurement of the gas volume flow rate obtained with standard vortex flow-meters.The proposed method is based on a correction factor determined through the application of an over-reading approach to a bluff body in mist flow.The correction factor is obtained from the slip velocity ratio,i.e.,the ratio of droplet velocity to gas velocity,based on the analysis of the fluid velocity distribution in the pipeline section.It also takes into account relevant theoretical arguments.It is shown that the predicted results fit the experimental results well.

A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

Multi-scale traffic vehicle detection based on faster ReCNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection.However,the environment of vehicle detection is particularly sophisticated in practical processes.It is comparatively difficult to detect vehicles of various scales in traffic scene images,because the vehicles partially obscured by green belts,roadblocks or other vehicles,as well as influence of some low illumination weather.In this paper,we present a model based on Faster ReCNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes.First,we proposed a Retinex-based image adaptive correction algorithm(RIAC)to enhance the traffic images in the dataset to reduce the influence of shadow and illumination,and improve the image quality.Second,in order to improve the feature expression of the backbone network,we conducted Neural Architecture Search(NAS)on the backbone network used for feature extraction of Faster ReCNN to generate the optimal cross-layer connection to extract multi-layer features more effectively.Third,we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets,and improve the robustness of the model for challenging targets such as small scale and severe occlusion.In the implementation of the model,K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model.Our model has been trained and tested on the UN-DETRAC dataset,and the obtained results indicate that our method has art-of-state detection performance.

The J_2 invariant relative configuration of spaceborne SAR interferometer for digital elevation measurement

A 3-craft formation configuration is proposed to perform the digital elevation model （DEM） for the distributed spacebome interferometric synthetic aperture radar （InSAR）, and it is optimized by the modified ant colony algorithm to have the best compatibility with J2 invariant orbits created by differential correction algorithm. The configuration has succeeded in assigning the across-track baseline to vary periodically and with its mean value equal to the optimal baseline determined by the relative height measurement accuracy. The required relationship between crafts＇ magnitudes and phases is formulated for the general case of interferometry measure from non-orthographic and non-lateral view. The J2 invariant configurations created by differential correction algorithm are employed to investigate their compatibility with the required configuration. The colony algorithm is applied to search the optimal configuration holding the near-constant across-track baseline under the J2 perturbation, and the absolute height measurement accuracy is preferable as expected.

Some mistakes in the algorithm for solving underflow problem in Baum-Welch algorithm and their corrections

Baum-Welch algorithm most likely results in underflow in practice. In some literatures, such as 'Scaling' algorithm was introduced to solve the problem. In applications, however, some mistakes were found in the equations presented in these literatures. The practical calculations show that the original algorithm often results in poor or even none convergence and rather higher error rate in speech recognition. The mistakes in these literatures and brings forward the correct equations are analysed. The speech recognition system using the revised algorithm can converge well and has lower error rate.

A BCH error correction scheme applied to FPGA with embedded memory

Given the potential for bit flipping of data on a memory medium,a high-speed parallel Bose-Chaudhuri-Hocquenghem(BCH)error correction scheme with modular characteristics,combining logic implementation and a look-up table,is proposed.It is suitable for data error correction on a modern field programmable gate array full with on-chip embedded memories.We elaborate on the optimization method for each part of the system and analyze the realization process of this scheme in the case of the BCH code with an information bit length of 1024 bits and a code length of 1068 bits that corrects the 4-bit error.

A Unified Approach for Developing EfficientAlgorithmic Programs

A unified approach called partition-and-recur for developing efficient and correct algorithmic programs is presented. An algorithm (represented by recurrence and initiation) is separated from program, and special attention is paid to algorithm manipulation rather than program calculus. An algorithm is exactly a set of mathematical formulae. It is easier for formal derivation and proof. After getting efficient and correct algorithm, a trivial transformation is used to get a final program. The approach covers several known algorithm design techniques, e.g. dynamic programming, greedy, divide-and-conquer and enumeration, etc. The techniques of partition and recurrence are not new. Partition is a general approach for dealing with complicated objects and is typically used in divide-and-conquer approach. Recurrence is used in algorithm analysis, in developing loop invariants and dynamic programming approach. The main contribution is combining two techniques used in typical algorithm development into a unified and systematic approach to develop general efficient algorithmic programs and presenting a new representation of algorithm that is easier for understanding and demonstrating the correctness and ingenuity of algorithmic programs.

Wavelength Dispersion Analysis on Fiber-Optic Raman Distributed Temperature Sensor System

The influence of the wavelength dispersion on the temperature accuracy of the Raman distributed temperature sensor system （RDTS） is analyzed in detail, and a simple correction algorithm is proposed to compensate the fiber position error caused by the wavelength dispersion. The principle of the proposed algorithm is described theoretically, and the correction on each point along the entire fiber is realized. Temperature simulation results validate that the temperature distortion is corrected and the temperature accuracy is effectively improved from ＋5 ℃ to ±1 ℃.