Method for pedestrian signal timing at signalized intersections

The pedestrian timing at signalized intersections is studied aiming at the problems of the inconsistency of the vehicular and pedestrian timing requirements and the insufficiency of pedestrian clearance. Based on the formulae of WALK and flashing DON＇T WALK （FDW） in the highway capacity manual （HCM）, the relationship between pedestrian signal indications and vehicular signal indications is discussed using the theory of traffic flow. Then, methods of pedestrian timing for different cases are established, particularly the methods of the pedestrian green adjustment. Ways of pedestrian crossing are analyzed for roadways with different forms and widths of the median island. The sampling values of calculation parameters are studied, and the recommended formulae of pedestrian timing for different conditions are presented.

Cell transmission model based traffic signal timing in oversaturated conditions

In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of constant values to simulate traffic flow movement, estimate the average delay of the network and search for an optimal traffic signal timing plan. A case study was given to demonstrate that the proposed methodology can capture unique phenomena in oversaturated conditions such as forward wave, spillback and lane entrance blockage. The results show that CTM underestimates travel time by 25% when compared to Simtraffic, while the enhanced CTM underestimates by only 3%. A second case study shows that a dynamic signal timing plan is superior to a fixed signal timing plan in the term of average delay.

Identification and optimization of traffic bottleneck with signal timing

In urban transportation network, traffic congestion is likely to occur at traffic bottlenecks. The signal timing at intersections together with static properties of left-turn and straight-through lanes of roads are two significant factors causing traffic bottlenecks. A discrete-time model of traffic bottleneck is hence developed to analyze these two factors, and a bottleneck indicator is introduced to estimate the comprehensive bottleneck degree of individual road in regional transportation networks universally, the identification approaches are presented to identify traffic bottlenecks, bottleneck-free roads, and bottle- neck-prone roads. Based on above work, the optimization method applies ant colony algorithm with ef- fective green time as decision variables to find out an optimal coordinated signal timing plan for a re- gional network. In addition, a real experimental transportation network is chosen to verify the valida- tion of bottleneck identification. The bottleneck identification approaches can explain the features of oc- currence and dissipation of traffic congestion in a certain extent, and the bottleneck optimization meth- od provides a new way to coordinate signal timing at intersections to mitigate traffic congestion.

Multi-phase signal setting and capacity of signalized intersection

A capacity model of multi-phase signalized intersections is derived by a stopping-line method. It is simplified with two normal situations： one situation involves one straight lane and one left-turn lane; the other situation involves two straight lanes and one left-turn lane. The results show that the capacity is mainly relative to signal cycle length, phase length, intersection layout and following time. With regard to the vehicles arrival rates, the optimal model is derived based on each phase＇s remaining time balance, and it is solved by Lagrange multipliers. Therefore, the calculation models of the optimal signal cycle length and phase lengths are derived and simplified. Compared to the existing models, the proposed model is more convenient and practical. Finally, a practical intersection is chosen and its signal cycles and phase lengths are calculated by the proposed model.

The application of breast DCE-MRI combined with time signal curve in diagnosing early breast cancer

Objective： The aim of this study was to investigate the application value of breast dynamic contrast-enhanced magnetic resonance imaging combined with time signal curve in diagnosis of early breast cancer. Methods： Conducted dynamic contrast-enhanced MRI and drew the time signal curves of breast lesions in 60 patients with breast disease （malignant 46, benign 14）. Results： Morphological features of malignant tumors mostly showed blurred or thin spiculate outlines, irregular shape or Iobular signs, signal heterogeneity or peripheral enhancement in dynamic contrast-enhanced MRI. Time signal curve showed type III or II. Morphologic features of benign tumors mostly showed clear edge, regular shape and homogeneous signal, or diffuse spot enhancement. Time signal curve showed type I or II. Conclusion： breast dynamic contrast enhanced scan in MRI can provide morphology and functional diagnosis information of the breast tissues. Dynamic contrast-enhanced MRI combined with time signal curve can further improve the accuracy of diagnosis of early breast cancer.

Impact of GPS Almanac Broadcast Strategy on the Signal Acquisition Time

This paper investigates the problem of almanac affecting the signal acquisition time with two constraints: different age of data and multi-sets of almanac. The contributions made in this paper include: 1) the exploiting of signal acquisition concept to extend well-known almanac function of predicting visible satellite and initializing signal acquisition to minimizing the signal acquisition time; 2) a model based on code phase and Doppler frequency to reflect the impact of multi-sets of almanac on the signal acquisition time; 3) the evaluation of the existing GPS almanac with different broadcast strategy. The theoretical analyses and simulations conducted on three sets of almanac show that the model proposed in this paper is general and efficient for almanac design and application.

Optimization of traffic signal parameters based on distribution of link travel time

In order to make full use of digital data, such as data extracted from electronic police video systems, and optimize intersection signal parameters, the theoretical distribution of the vehicle's road travel time must first be determined. The intersection signal cycle and the green splits were optimized simultaneously, and the system total travel time was selected as the optimization goal. The distribution of the vehicle's link travel time is the combined results of the flow composition, road marking, the form of control, and the driver's driving habits. The method proposed has 15% lower system total stop delay and fewer total stops than the method of TRRL(Transport and Road Research Laboratory) in England and the method of ARRB(Australian Road Research Board) in Australia. This method can save 0.5% total travel time and will be easier to understand and test, which establishes a causal relationship between optimal results and specific forms of road segment management, such as speed limits.

Acceleration of optical coherence tomography signal processing by multi-graphics processing units

A multi-GPU system designed for high-speed,real-time signal processing of optical coherencetomography(OCT)is described herein.For the OCT data sampled in linear wave numbers,themaximum procesing rates reached 2.95 MHz for 1024-OCT and 1.96 MHz for 2048-OCT.Data sampled using linear wavelengths were re-sampled using a time-domain interpolation method and zero-padding interpolation method to improve image quality.The maximum processing rates for1024-OCT reached 2.16 MHz for the time-domain method and 1.26 MHz for the zero-paddingmethod.The maximum processing rates for 2048-0CT reached_1.58 MHz,and 0.68 MHz,respectively.This method is capable of high-speed,real-time processing for O CT systems.

Optimization of Crosswalk Width Based on Bi-Directional Pedestrian Crossing Time at Signalized Intersections

The effects of the interactions between bi-directional pedestrians on the crossing time and the crosswalk width are studied. Firstly,the crossing process of bi-directional pedestrians is analyzed.The total crosswalk time is divided into a discharge time and a crossing time. The interactions between bi-directional pedestrians are quantified with the drag force theory. Then,a model is developed to study the crossing time based on the kinetic energy theory and momentum theory. Subsequently,the related parameters of the proposed model are calibrated with observed information. The relationships among crosswalk width,signal time,pedestrian volume and level of service are simulated with the proposed model. The results are verified and compared with other models. The proposed model has an absolute value of relative error of 9. 38%,which is smaller than that of the Alhajyaseen model（ 15. 26%） and Highway Capacity Manual（ HCM） model（ 12. 42%）. Finally,suggested crosswalk widths at different conditions are successfully estimated with the proposed crossing time model.

An attention graph stacked autoencoder for anomaly detection of electro-mechanical actuator using spatio-temporal multivariate signals

Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of airplanes.It is difficult or even impossible to collect enough labeled failure or degradation data from actual EMA.The autoencoder based on reconstruction loss is a popular model that can carry out anomaly detection with only consideration of normal training data,while it fails to capture spatio-temporal information from multivariate time series signals of multiple monitoring sensors.To mine the spatio-temporal information from multivariate time series signals,this paper proposes an attention graph stacked autoencoder for EMA anomaly detection.Firstly,attention graph con-volution is introduced into autoencoder to convolve temporal information from neighbor features to current features based on different weight attentions.Secondly,stacked autoencoder is applied to mine spatial information from those new aggregated temporal features.Finally,based on the bench-mark reconstruction loss of normal training data,different health thresholds calculated by several statistic indicators can carry out anomaly detection for new testing data.In comparison with tra-ditional stacked autoencoder,the proposed model could obtain higher fault detection rate and lower false alarm rate in EMA anomaly detection experiment.

High-Resolution Traffic Flow Prediction Model Based on Deep Learning

The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing optimization.Based on the view of the platoon dispersion model,the relationship between vehicle arrival at the downstream intersection and vehicle departure from the upstream intersection was analyzed.Then,a high-resolution traffic flow prediction model based on deep learning was developed.The departure flow rate from the upstream and the arrival flow rate at the downstream intersection was taking as the input and output in the proposed model,respectively.Finally,the parameters of the proposed model were trained by the field data,and the proposed model was implemented to forecast the arrival flow rate of the downstream intersection.Results show that the proposed model can better capture the fluctuant traffic flow and reduced MAE,MRE,and RMSE by 9.53%,39.92%,and 3.56%,respectively,compared with traditional models and algorithms,such as Robert­son's model and artificial neural network.Therefore,the proposed model can be applied for realtime adaptive signal timing optimization.

Usefulness of dynamic contrast-enhanced MR imaging in the evaluation of pulmonary isolated lesions

Objective： The aim of our study was to investigate the value of dynamic contrast-enhanced MRI for evaluating differential diagnosis of pulmonary isolated lesions. Methods： Twenty-nine consecutive patients enrolled in this study, all of whom underwent DCE-MRI examinations and received a histologic and clinical diagnosis. Among these, lung tuberculoma 7 cases, harmatoma 3 cases, peripheral lung cancer 19 cases. DCE-MRI was acquired with 3D LAVA technique, total 18 phases were acquired, scanner time of per phase was 5-7″. After contrasting agent, twice successive scanning was acquired at 10″ and 50″. Then 1′30″, 2′, 2′30″, 3′, 3′30″, 4′, 5′, 6′, 7′, 8′, 9′, 10′, 11′, 12′ performed scanning. Region of interest was placed on the Maximum level in the tumors. According to Schaefer＇s standard, four types of time signal intensity curve （TIC） were classified, which were A, B, C and D. Compared the dynamic parameters between benign and malignant nodules. Results： Lung tuberculoma may display three curves： A type 1 case, ring-shaped enhancement 4 cases （periphery ring A type, central region D type）, D type 2 cases. Harmatoma may display three curves： A type 1 case, C type 2 case. Peripheral lung cancer may display A type. Except 2 cases D type lung tuberculoma, we compared curve data of 8 cases benign nodules （including tuberculoma Atype and periphery ring Atype, harmatoma Atype and C type） and lung cancer. SlEP%： benign nodules 0.7885 ±0.5543, lung cancer 1.2623 ±0.3059, P 〈 0.05; MER： benign nodules 1.0007 ± 0.4251, lung cancer 1.3694 ±0.2740, P 〈 0.05; washout： P 〉 0.05. Conclusion： Lung MR imaging is helpful to diagnosis and differential diagnosis of isolated benign and malignant nodules. SIEP% and MER could offer valuable information. The evolution of global tuberculosis may be from A type to ring-shaped ennoblement to D type. It was easy to do right diagnosis to lung tuberculoma with ring-shaped ennoblement and D type. Peripheral lung cancer commonly displayed A type and needed identification with acute inflammation. So, it is important to anti-inflammatory follow-up for a few A type nodules.

Investigation on interaction and characteristics of traffic flows in urban elevated road-ramp-ground road system

The rapid development of the automobile industry and the relative lag of the road construction have constituted a prominent contradiction all over the world, particularly, in most of large cities. To cope with it, elevated roads have been built in many cities both at home and abroad. However, traffic jams frequently appear on elevated roads immediately after the completion of their construction. The awkward situation mainly results from the planning bug or the unsuitable control, apart from drastic increase in transportation demand. Elevated roads,

Mammography combined with breast dynamic contrast-enhanced-magnetic resonance imaging for the diagnosis of early breast cancer

Objective The aim of this study was to investigate the application of mammography combined with breast dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) for the diagnosis of early breast cancer. Methods Mammography and DCE-MRI were performed for 120 patients with breast cancer(malignant, 102; benign; 18). Results The sensitivity of mammography for early diagnosis of breast cancer was 66.67%, specificity was 77.78%, and accuracy was 68.33%. The sensitivity of MRI for early diagnosis of breast cancer was 94.12%, specificity was 88.89%, and accuracy was 93.33%. However, the sensitivity of mammography combined with DCE-MRI volume imaging with enhanced water signal(VIEWS) scanning for early diagnosis of breast cancer was 97.06%, specificity was 94.44%, and accuracy was 96.67%. Conclusion Mammography combined with DCE-MRI increased the sensitivity, specificity, and accuracy of diagnosing early breast cancer.

Underdetermined Blind Source Separation of Adjacent Satellite Interference Based on Sparseness

The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform(STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.

Identifying the occurrence time of an impending mainshock:a very recent case

The procedure by means of which the occurrence time of an impending mainshock can be identified by analyzing in natural time the seismicity in the candidate area subsequent to the recording of a precursory seismic electric signals（SES） activity is reviewed. Here, we report the application of this procedure to an MW5.4 mainshock that occurred in Greece on 17 November 2014. This mainshock（which is pretty rare since it is the strongest in that area for more than half a century） was preceded by an SES activity recorded on 27 July 2014, and the results of the natural time analysis reveal that the system approached the critical point（mainshock occurrence） early in the morning on 15 November 2014.

A time domain correction method for Doppler-distortion signal from acoustic moving source

Doppler effect widely exists in the signal from the moving acoustic source. In order to solve such problems as frequency shift and frequency band expansion, a time domain cor- rection method is presented in this paper. First, the discrete time vector for interpolation and the amplitude restoration formula is derived based on the moving relationship and the Morse acoustic theory, then the amplitude weights are corrected and the distortion signal is interpolated. Every point of the discrete signal is operated separately in time domain. Compared with the existing frequency domain methods, this method does not need to know the characteristic frequency beforehand and would not be influenced by the blending of the frequency band. Hence, this method can be employed to correct multiple frequency signals and it is also a simple and effective Doppler effect reduction method.

Dispersion-Induced Waveform Distortion Detection in 42.7 Gbps CS-RZ Signals by Optical Time Domain Level Monitoring

We propose a technique for chromatic dispersion monitoring based on optical time domain level monitoring. Experimental and simulation results show that the technique is effective for the monitoring of dispersion in 42.7-Gbps CS-RZ signals for dynamic dispersion compensation.

Alteration of the temporal association between courtship audio and visual components affects female sexual response

Some multimodal signals-that is,occurring in more than one sensory modality-appear to carry additional information which is not present when component signals are presented separately.To understand the function of male ring dove’s(Streptopelia risoria)multimodal courtship,we used audiovisual playback of male displays to investigate female response to stimuli differing in their audiovisual timing.From natural courtship recordings,we created a shifted stimulus where audio was shifted relative to video by afixed value and a jittered stimulus,where each call was moved randomly along the visual channel.We presented 3 groups of females with the same stimulus type,that is,control,shifted,and jittered,for 7 days.We recorded their behavior and assessed pre-and post-test blood estradiol concentration.We found that playback exposure increased estradiol levels,confirming that this technique can be efficiently used to study doves’sexual communication.Additionally,chasing behavior(indicating sexual stimulation)increased over experimental days only in the control condition,suggesting a role of multimodal timing on female response.This stresses the importance of signal configuration in multimodal communication,as additional information is likely to be contained in the temporal association between modalities.