Comparison between ozonesonde measurements and satellite retrievals over Beijing,China

从2013年开始,作者团队使用自主研发电化学原理臭氧探空仪在华北平原北京地区进行每周一次观测.本研究首次使用2013-2019年期间北京地区臭氧探空数据评估Aqua卫星搭载大气红外探测仪(AIRS)和Aura卫星搭载微波临边探测器(MLS)反演垂直臭氧廓线,并对比臭氧探空,AIRS和Aura卫星搭载臭氧监测仪(OMI)臭氧柱总量结果.尽管臭氧探空与卫星反演垂直臭氧廓线在局部高度处差异较大,但整体来说两者较为接近(相对偏差大多<10%).臭氧探空,AIRS和OMI三种仪器测量臭氧柱总量的年变化特征较为一致,其年均臭氧柱总量分别为351.8±18.4 DU,348.8±19.5 DU和336.9±14.2 DU.后续对国内多站点观测数据分析将有助于进一步理解臭氧探空与卫星反演臭氧资料在不同区域的一致性.

Modelling of a WDM Network Using Graph Theory and Dijkstra Algorithm for Traffic Redirection

Optical transport networks are now the basic infrastructure of modern communications systems, including the SDH and WDM backbone network of local network operators, in the case of Cameroon. Given the colossal investments required to deploy these networks, particularly related to the cost of equipment (optical fibers, transponders and multiplexers), the optimization of bandwidth and dynamic allocation of resources is essential to control operating costs and ensure continuity of service. Automatic switching technology for optical networks brings intelligence to the control plane to fully facilitate bandwidth utilization, traffic redirection, and automatic configuration of end-to-end services. This paper considers a local network operator’s WDM network without the implementation of the automatic switching technology, develops a network modeling software platform called Graphic Networks and using graph theory integrates a particularity of the automatic switching technology, which is the automatic rerouting of traffic in case of incident in the network. The incidents considered here are those links or route failures and node failures.

Structures, stabilities, and electronic properties of F-doped Sin （n=1～12） clusters：Density functional theory investigation

The geometries, stabilities, and electronic properties of FSin （n=1～12） clusters are systematically investigated by using first-principles calculations based on the hybrid density-functional theory at the B3LYP/6-311G level. The geometries are found to undergo a structural change from two-dimensional to three-dimensional structure when the cluster size n equals 3. On the basis of the obtained lowest-energy geometries, the size dependencies of cluster properties, such as averaged binding energy, fragmentation energy, second-order energy difference, HOMO–LUMO （highest occupied molecular orbital–lowest unoccupied molecular orbital） gap and chemical hardness, are discussed. In addition, natural population analysis indicates that the F atom in the most stable FSin cluster is recorded as being negative and the charges always transfer from Si atoms to the F atom in the FSin clusters.

Structure, stability and electronic properties of SrSi_n(n= 1–12) clusters:Density-functional theory investigation

Geometric structures, stabilities, and electronic properties of SrSin(n = 1–12) clusters have been investigated using the density-functional theory within the generalized gradient approximation. The optimized geometries indicate that one Si atom capped on SrSin 1structure and Sr atom capped Sinstructure for difference SrSinclusters in size are two dominant growth patterns. The calculated average binding energy, fragmentation energy, second-order energy difference, the highest occupied molecular orbital, and the lowest unoccupied molecular orbital(HOMO–LUMO) gaps show that the doping of Sr atom can enhance the chemical activity of the silicon framework. The relative stability of SrSi9is the strongest among the SrSinclusters. According to the mulliken population and natural population analysis, it is found that the charge in SrSin clusters transfer from Sr atom to the Sinhost. In addition, the vertical ionization potential, vertical electron affinity, and chemical hardness are also discussed and compared.

Strategy dominance mechanism of autonomous collaboration in unmanned swarm within the framework of public goods game

The key advantage of unmanned swarm operation is its autonomous cooperation. How to improve the proportion of cooperators is one of the key issues of autonomous collaboration in unmanned swarm operations. This work proposes a strategy dominance mechanism of autonomous collaboration in unmanned swarm within the framework of public goods game. It starts with the requirement analysis of autonomous collaboration in unmanned swarm;and an aspiration-driven multiplayer evolutionary game model is established based on the requirement. Then the average abundance function and strategy dominance condition of the model are constructed by theoretical derivation. Furthermore, the evolutionary mechanism of parameter adjustment in swarm cooperation is revealed via simulation,and the influences of the multiplication factor r, aspiration levelα, threshold m and other parameters on the strategy dominance conditions were simulated for both linear and threshold public goods games(PGGs) to determine the strategy dominance characteristics;Finally, deliberate proposals are suggested to provide a meaningful exploration in the actual control of unmanned swarm cooperation.

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

Preventive maintenance in the transformer is performed through a dif-ferential relay protection system,and it protects the transformer from internal and external faults.However,the Current Transformer(CT)in the differential protec-tion system mal-operates during inrush currents.CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays.Moreover,iden-tification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed.For the above problem,continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the trip-ping in relay due to inrush or internal fault.The transformer’s internal fault leads to high breathing process in the transformer breather,never for inrush currents.During inrush currents,CT temperature is increased.Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pix-els detect the causes of CT saturation and differentiates maloperation.Hybrid wavelet threshold image analytics(HWT-IA)based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95%for identification of the false tripping of differential protection system of transformer.

Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

New Ecg Signal Compression Model Based on Set Theory Applied to Images

Cardiovascular diseases are the origin of many causes of death worldwide. They impose on practitioners optimal diagnostic methods such as telemedicine in order to be able to quickly detect anomalies for daily care and monitoring of patients. The Electrocardiogram (ECG) is an examination that can detect abnormal functioning of the heart and generates a large number of digital data which can be stored or transmitted for further analysis. For storage or transmission purposes, one of the challenges is to reduce the space occupied by ECG signal and for that, it is important to offer more and more efficient algorithms capable of achieving high compression rates, while offering a good quality of reconstruction in a relatively short time. We propose in this paper a new ECG compression scheme that is based on a subset of signal splitting and 2D processing, the wavelet transform (DWT) and SPIHT coding which has proved their worth in the field of signal processing and compression. They are exploited for decorrelation and coding of the signal. The results obtained are significant and offer many perspectives.

Design of a Neural Network Based Stable State Observer for Mimo Systems

MIMO (Multiple Input Multiple Output) is a key technology underpinning fourth generation or 4G networks. This technology allows 4G networks to increase throughput. However, the dynamics of the MIMO system are not under control due to the many uncertainties that destabilize the system. This work is therefore very relevant in the sense that an observer can be used to monitor the dynamics of such a system. This work presents a neuro-adaptive observer based on a radial basis function neural network for generic non-linear MIMO systems. Unlike most neuro-adaptive observers, the proposed observer uses a neural network that is non-linear in its parameters. It can therefore be applied to systems with high degrees of nonlinearity without any a priori knowledge of the system dynamics. Indeed, in addition to the fact that neural networks are very good nonlinear approximators, their adaptive behavior makes them powerful tools for observing the state without any a priori knowledge of the dynamics of the system. The learning rule of the neural network is an approach based on the modified backpropagation algorithm: A term has been added to guarantee the robustness of the observer. The proposed approach is not limited by a strong assumption. The stability of the neuro-adaptive observer is demonstrated by the direct Lyapunov method. Simulation results are presented in the context of MIMO signal transmission applied in LTE, to demonstrate the performance of our observer.

Machine Learning-Based Approach for Identification of SIM Box Bypass Fraud in a Telecom Network Based on CDR Analysis: Case of a Fixed and Mobile Operator in Cameroon

In the telecommunications sector, companies suffer serious damages due to fraud, especially in Africa. One of the main types of fraud is SIM box bypass fraud, which includes using SIM cards to divert incoming international calls from mobile operators creating massive losses of revenue. In order to provide a solution to these shortcomings that apply almost to all network operators, we developed intelligent algorithms that exploit huge amounts of data from mobile operators and that detect fraud by analyzing CDRs from voice calls. In this paper we used three classification techniques: Random Forest, Support Vector Machine (SVM) and XGBoost to detect this type of fraud;we compared the performance of these different algorithms to evaluate the model by using data collected from an operator’s network in Cameroon. The algorithm that produced a better performance was the Random Forest with 92% accuracy, so we effectuated the detection of existing fraudulent numbers on the telecommunications operator’s network.

Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4G and 5G networks.

COST 231-Hata Propagation Model Optimization in 1800 MHz Band Based on Magnetic Optimization Algorithm: Application to the City of Limbé

Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.

Okumura Hata Propagation Model Optimization in 400 MHz Band Based on Differential Evolution Algorithm: Application to the City of Bertoua

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon.

基于数据库的电力设备故障诊断模糊专家系统的设计与实现

采用ＳＱＬ Ａｎｙｗｈｅｒｅ ５．０设计知识库、 ＰｏｗｅｒＢｕｉｌｄｅｒ６．５编程实现了电力设备故障诊断模糊专家系统，其知识的表示采用了模糊产生式表示法，引进了模糊匹配与加权模糊逻辑进行模糊推理，实现了一种较为理想的非精确推理．

Improved Nonlinear Equation Method for Numerical Prediction of Jominy End-Quench Curves

Without considering the effects of alloying interaction on the Jominy end-quench curves, the prediction resuits obtained by YU Bai-hai＇s nonlinear equation method for multi-alloying steels were different from those experimental ones reported in literature. Some alloying elements have marked influence on Jominy end-quench curves of steels. An improved mathematical model for simulating the Jominy end-quench curves is proposed by introducing a parameter named alloying interactions equivalent （Le）. With the improved model, the Jominy end-quench curves of steels so obtained agree very well with the experimental ones.

Improved Photoresponse of UV Photodetectors by the Incorporation of Plasmonic Nanoparticles on Ga N Through the Resonant Coupling of Localized Surface Plasmon Resonance

Very small metallic nanostructures,i.e.,plasmonic nanoparticles(NPs),can demonstrate the localized surface plasmon resonance(LSPR)e ect,a characteristic of the strong light absorption,scattering and localized electromagnetic field via the collective oscillation of surface electrons upon on the excitation by the incident photons.The LSPR of plasmonic NPs can significantly improve the photoresponse of the photodetectors.In this work,significantly enhanced photoresponse of UV photodetectors is demonstrated by the incorporation of various plasmonic NPs in the detector architecture.Various size and elemental composition of monometallic Ag and Au NPs,as well as bimetallic alloy Ag Au NPs,are fabricated on Ga N(0001)by the solid-state dewetting approach.The photoresponse of various NPs are tailored based on the geometric and elemental evolution of NPs,resulting in the highly enhanced photoresponsivity of 112 A W-1,detectivity of 2.4×1012 Jones and external quantum e ciency of 3.6×104%with the high Ag percentage of Ag Au alloy NPs at a low bias of 0.1 V.The Ag Au alloy NP detector also demonstrates a fast photoresponse with the relatively short rise and fall time of less than 160 and 630 ms,respectively.The improved photoresponse with the Ag Au alloy NPs is correlated with the simultaneous e ect of strong plasmon absorption and scattering,increased injection of hot electrons into the Ga N conduction band and reduced barrier height at the alloy NPs/Ga N interface.

Application of a new feature extraction and optimization method to surface defect recognition of cold rolled strips

Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform （FFF） and sum of valid pixels （SVP）, and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.

Hawking radiation of a uniformly accelerating black hole

In this paper, we study the Hawking radiation via tunnelling from a uniformly accelerating black hole. Although the Bekenstein-Hawking entropy is proportional also to the area of the event horizon, the radius of it, rH, is a function of 0, which leads to the difficulties in the calculation of the emission rate. In order to overcome the mathematical difficulties, we propose a new technique to calculate the emission rate and the result obtained is reasonable.

Cued search algorithm with uncertain detection performance for phased array radars

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

Automatic Identification of Clear-Air Echoes Based on Millimeter-wave Cloud Radar Measurements

Millimeter-wave cloud radar(MMCR)provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds.Therefore,MMCR has been widely applied in cloud observations.However,due to the influence of non-meteorological factors such as insects,the cloud observations are often contaminated by non-meteorological echoes in the clear air,known as clear-air echoes.It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background.The characteristics of clear-air echoes are studied here by combining data from four devices:an MMCR,a laser-ceilometer,an L-band radiosonde,and an all-sky camera.In addition,a new algorithm,which includes feature extraction,feature selection,and classification,is proposed to achieve the automatic identification of clear-air echoes.The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions.The recognition accuracy can reach up to 95.86%for the simple cases when cloud echoes and clear-air echoes are separate,and 88.38%for the complicated cases when low cloud echoes and clear-air echoes are mixed.