Pilot protection of hybrid MMC DC grid based on active detection

Considering the advantages and limitations of traditional identification method,combined with the strategy of active detection,the principle of DC grid pilot protection based on active detection is proposed to improve the sensitivity and reliability of hybrid MMC DC grid protection,and to ensure the accurate identification of fault areas in DC grid.By using the DC fault ride-through control strategy of the hybrid sub-module MMC,the fault current at the converter station DC terminal is limited.Based on the high controllability of hybrid MMC,sinusoidal fault detection signals with the same frequency are injected into the line at each converter station.Based on model recognition,the capacitance model condition is satisfied by the detected signals at both ends during external faults whereas not satisfied during internal faults.The Spearman correlation coefficients is then introduced,and the correlation discriminant of capacitance model is constructed to realize fault area discrimination of DC grid.The simulation results show that the active detection protection scheme proposed in this paper can accurately identify the fault area of DC grid,and is not affected by fault impedance and has low sampling rate requirement.

An echo detection algorithm for underwater continuous wave active detection

The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the heterodyne signal, an algorithm with the structure of heterodyne-Practional Fourier Transform （FRFT） was proposed. To reduce the computation of searching targets in a two-dimensional FRFT result, the heterodyne signal would be processed by FRFT at a specific order, after Radon-Ambiguity Transform （RAT） was applied to estimate the sweep rate of the signal. Simulations proved that the algorithm can eliminate the coupling phenomenon of distance and velocity of LFMCW, and estimate targets＇ parameters accurately. The lake trial results showed that the processing gain of LFMCW processed by the algorithm in this paper was 13 dB better than that of the LFM processed by matched filter. The research results indicated that the algorithm applied in LFMCW underwater detection was feasible and effective, and it could estimate targets＇ parameters accurately and obtain a good detection performance.

Active User and Data Detection for Uplink Grant-free NOMA Systems

This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.

Geospatial Analytics for COVID-19 Active Case Detection

Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.

Multi-Panel Extra-Large Scale MIMO Based Joint Activity Detection and Channel Estimation for Near-Field Massive IoT Access

The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.

Improved Active Islanding Detection Technique for Multi-Inverter Power System

Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.

Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.

Adaptive and augmented active anomaly detection on dynamic network traffic streams

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network attacks.However,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams.To address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic.On one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of anomalies.On the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model updating.Extensive experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

Investigating the minimum detectable activity concentration and contributing factors in airborne gamma-ray spectrometry

In this study,the theory of minimum detectable activity concentration(MDAC)for airborne gamma-ray spectrometry(AGS)was derived,and the relationship between the MDAC and the intrinsic effi-ciency of a scintillation counter,volume,and energy res-olution of scintillation crystals,and flight altitude of an aircraft was investigated.To verify this theory,experi-mental devices based on NaI and CeBr 3 scintillation counters were prepared,and the potassium,uranium,and thorium contents in calibration pads obtained via the stripping ratio method and theory were compared.The MDACs of AGS under different conditions were calculated and analyzed using the proposed theory and the Monte Carlo method.The relative errors found via a comparison of the experimental and theoretical results were less than 4%.The theory of MDAC can guide the work of AGS in probing areas with low radioactivity.

Activity Detection for Enhanced Configured-Grant:A Practical Perspective

We try to extend the current configuredgrant(CG)uplink scheme in 5G New Radio(NR)to support massive potential users and study activity detection under this scenario.Characteristics of the continuously varying channel and the multiple repetition scheme are utilized to improve the detection accuracy,which can be an enhancement to existing activity detection algorithms.Numerical results under 3 GPP TDL(Tapped Delay Line)fading channel show the superiority of our algorithm.And system-level simulation reveals that enhancements on activity detection can improve reliability and reduce latency.

Development of a Detection Method for Assaying Calf Intestine Alkaline Phosphatase Activity

[ Objective] This study was to develop a detection method for assaying calf intestine alkaline phasphatase activity in scientific research. [ Method ] By simulating the conditions and buffers for assaying calf intestine alkaline phosphatase used in the scientific research, the parameters influencing substmte concentration, reaction duration, eoloration time and buffer pH were optimized. [ Result] The activity of alkaline phosphatase detected varied hugely among different buffers, and potassium ferricyanide solution added with boracic acid achieved the stable coloration. The optimal water bath time was determined as 10 rain, and the substrate concentration was optimized as 0.04 moL/L. The increasing temperature did not have a large influence on low temperature enzymatic activity while did on high coneentration enzymatic activity. When the buffer pH was 7.0 - 8.0, the detection stability of alkaline phosphatase could be maintained well The ion intensity of buff- er had little effects on alkaline phasphatase activity. [ Conclusion] A detection method for assaying calf intestine alkaline phosphatase activity in scientific research was successfully developed in this study.

Clinical Detection of Peripheral Blood Natural Killer Cell Activity

Natural Killer (NK) cells are specific immune cells in human immune system. They have a quick effect and can exert a cytotoxic function without prior sensitization, and they show great application potential in cell-based immunotherapy, anti-infection in vivo. NK cell activity in peripheral blood can be used as one of the biomarkers of immune function response. It has a great positive guiding significance for the clinical prognosis of tumor patients, the prevention of cancer and anti-aging. The clinical detection strategies of NK cell activity in circulation mainly grouped into five types: methyl thiazolyl tetrazolium colorimetric, lactate dehydrogenase release, radionuclide labeling, flow cytometry and NK Vue cytokine release method. It has played an important role in different stages of clinical application development. This paper will make a comparative review of the above-mentioned detection strategies for the NK cell activity.

Massive Unsourced Random Access Under Carrier Frequency Offset

Unsourced random access(URA)is a new perspective of massive access which aims at supporting numerous machine-type users.With the appearance of carrier frequency offset(CFO),joint activity detection and channel estimation,which is vital for multiple-input and multiple-output URA,is a challenging task.To handle the phase corruption of channel measurements under CFO,a novel compressed sensing algorithm is proposed,leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel.An uncoupled transmission scheme is proposed to reduce system complexity,where slot-emitted messages are reorganized relying on clustering unique user channels.Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

An Overview of Passive and Active Dust Detection Methods Using Satellite Measurements

In this paper,the methods to detect dust based on passive and active measurements from satellites have been summarized.These include the visible and infrared（VIR） method,thermal infrared（TIR） method,microwave polarized index（MPI） method,active lidar-based method,and combined lidar and infrared measurement（CLIM） method.The VIR method can identify dust during daytime.Using measurements at wavelengths of 8.5,11.0,and 12.0 fan,the TIR method can distinguish dust from other types of aerosols and cloud,and identify the occurrence of dust over bright surfaces and during night.Since neither the VIR nor the TIR method can penetrate ice clouds,they cannot detect dust beneath ice clouds.The MPI method,however,can identify about 85%of the dust beneath ice clouds.Meanwhile,the active lidar-based method,which uses the Cloud-Aerosol Lidar with Orthogonal Polarization（CALIOP） data and five-dimensional probability distribution functions,can provide very high-resolution vertical profiles of dust aerosols.Nonetheless,as the signals from dense dust and thin clouds are similar in the CALIOP measurements,the lidar-based method may fail to distinguish between them,especially over dust source regions.To address this issue,the CLIM method was developed,which takes the advantages of both TIR measurements（to discriminate between ice cloud and dense dust layers） and lidar measurements（to detect thin dust and water cloud layers）.The results obtained by using the new CLIM method show that the ratio of dust misclassification has been significantly reduced.Finally,a concept module for an integrated multi-satellites dust detection system was proposed to overcome some of the weaknesses inherent in the single-sensor dust detection.

Fast Echo Canceller in IP Telephony Gateway

The length of the echo path in the IP telephony system is very long. Generally, the echo canceller is implemented on the IP telephony gateway which needs to perform concurrently multi-channel echo cancellation and voice compression. Hence, the most key technique to design the echo canceller is to reduce greatly the computational requirement. For this reason a number of innovative features to implement a fast echo canceller are presented. The key components of this canceller include: the separation of adaptive and cancel filters, non-real-time adaptation and real-time cancellation, sharing VAD algorithms with the speech codec, the incorporation of delay indexing with zero coefficients, and windowing the adaptive filter coefficients to reduce the cost of DSP during the cancellation. Finally, the performance of the echo canceller is summarized; the results of evaluation show that the performance gains for echo cancellation are significant.

A LoRaWAN Access Technology Based on Channel Adaptive Adjustment

Low-power wide area network(LPWAN)has developed rapidly in recent years and is widely used in various Internet of Things(IoT)services.In order to reduce cost and power consumption,wide coverage,LPWAN tends to use simple channel access control protocols,such as the Aloha protocol.This protocol is simple with poor extension capability.In high-density environment,Aloha protocol will lead to low channel utilization,prolonged access and high conflict probability.Therefore,in order to solve the above problems,we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol,that is,a dynamic listening backoff mechanism.We combine the improved“listen first and then talk”(LBT)mechanism with the current state of the channel to adaptively adjust the size of the backoff window.The theoretical analysis and simulation results show that the proposed mechanism have a better performance than the existing mechanism,it can reduce conflicts in dense environments.By comparison,the packet transmission success rate is increased by 17%.

Audio-visual voice activity detection

In speech signal processing systems,frame-energy based voice activity detection(VAD)method may be interfered with the background noise and non-stationary characteristic of the frame-energy in voice segment.The purpose of this paper is to improve the performance and robustness of VAD by introducing visual information.Meanwhile,data-driven linear transformation is adopted in visual feature extraction,and a general statistical VAD model is designed.Using the general model and a two-stage fusion strategy presented in this paper,a concrete multimodal VAD system is built.Experiments show that a 55.0%relative reduction in frame error rate and a 98.5%relative reduction in sentence-breaking error rate are obtained when using multimodal VAD,compared to frame-energy based audio VAD.The results show that using multimodal method,sentence-breaking errors are almost avoided,and frame-detection performance is clearly improved,which proves the effectiveness of the visual modal in VAD.

Novel DTD and VAD assisted voice detection algorithm for VoIP systems

Echo cancellation plays an important role in current Internet protocol（IP） based voice interactive systems. Voice state detection is an essential part in echo cancellation. It mainly comprises two parts： double talk detection（DTD） and voice activity detection（VAD）. DTD is used to detect doubletalk and prevent filter divergence in the presence of near-end speech, and VAD is used to determine the near-end voice activity and output silence indicator when near-end is silent. However, DTD straightforwardly proceeded may mistakenly declare double talk under double silent condition, coefficients update under the far-end silence condition may lead to filter divergence, and current VAD algorithms may misjudge the residual echo from the near end to be far-end voice. Therefore, a voice detection algorithm combining DTD and far-end VAD is proposed. DTD is implemented when VAD declares far-end speech, filtering and coefficients update will be halted when VAD declares far-end silence, and the far-end VAD adopted is multi-feature VAD based on short-time energy and correlation. The new algorithm can improve the accuracy of DTD, prevent filter divergence, and exclude the circumstance that far-end signal only contains residual echo from near end. Actual test results show that the voice state decision of the new algorithm is accurate, and the performance of echo cancellation is improved.