Short-Term Forecasting of Emergency Inpatient Flow

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.     

多尺度高斯模型的短期负荷预测

为了准确地进行电力负荷的短期预测,借鉴小波分析中对函数进行多尺度表示的思想,文中在高斯过程模型的基础上提出了多尺度高斯过程模型.通过选择合适的尺度参数,采用计算预测均方误差值大小的策略获取最佳延迟时间和最优嵌入维数对,然后对西北某地区电力系统进行短期负荷预测.与传统的支持向量机、径向基函数网负荷预测方法相比,基于多尺度高斯过程模型的短期负荷预测方法预测精度与支持向量机方法相当,性能优于径向基函数.     

WNN在短期电力负荷预测中的应用

通过对小波神经网络和BP神经网络的结构和算法进行理论分析,并对实际电力负荷预测算例进行对比研究,结论证明小波神经网络本身适合对波动性的信号进行预测,而且在神经网络节点数目相同的情况下,小波神经网络比BP神经网络具有更高的预测精度,因此采用小波神经网络有利于减少隐节点数目。     

Techniques for Short-Term Predictions of Atmospheric Noise Levels

Short-term predictions of operational characteristics of ionospheric radio communication such as MUF, atmospheric noise, etc., are likely to lead to more efficient use of the HF spectrum. There are no techniques available for the short-term predictions of atmospheric noise so far. The paper describes two techniques for the purpose viz., autocorrelation technique and source location technique. The former predicts the noise level at a specified hour from the measurement at an earlier hour since atmospheric noise level variations constitute a Markov process. The latter predicts the noise level by predicting the type of the day from the characteristics observed at 16 hrs. Prediction accuracies are discussed, and the possibility of refinements is investigated. Possible system applications are discussed, and a typical calculation is carried out which shows that economies in the radiated power of the order of 10 dB are possible.     

中国电信发展的近期目标及主要措施

本文简述了我国电信发展的环境与条件，近期电信工作的任务和目标。最后指出实现电信发展目标的主要措施。     

一种面向短时突发信号的高精度同步方案设计

针对短时突发通信存在捕获时间长、算法复杂度高、资源开销大等问题,文中设计了一种高精度同步方案。该方案自定义收发双方帧结构,采用独特码差分匹配算法实现信号检测和帧同步,并采用基于导频累积量的前向估计算法实现频偏估计和校正。为兼顾频偏估计精度和算法复杂度,对独特码采用滑动相关算法实现初始相位捕获,使系统结构紧凑,更加适合FPGA硬件实现。理论分析和软件仿真表明,该方案在低信噪比和高动态范围条件下具有较好的接收性能,频率估计精度能够达到符号速率的万分之一。文中同时进行了硬件协同仿真与验证,并给出了相应的仿真波形。     

Short-Term Time Characteristics of Atmospheric Radio Noise Above Different Thresholds

Short-term time characteristics of burst and quasi- continuous forms of atmospheric radio noise were investigated at Bangalore at a number of frequencies in the range 100 kHz-9 MHz and over a 300-Hz-16-kHz range of bandwidth. It was found that for all frequencies, bandwidths, and threshold levels the duration and spacing follow log-normal distributions. The duration of a pulse above a particular threshold is statistically independent of its spacing from an adjacent pulse. Further, the average duration varies approximately linearly with the threshold level. These results are expected to be of use in planning digital communications where atmospheric noise is the limiting factor in reception capability.     

Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.

     

Deep Graph-Long Short-Term Memory: A Deep Learning Based Approach for Text Classification

Wireless Personal Communications - Multi-label text classification is a challenging task in many real applications. Mostly, in all the traditional techniques, word2vec is used to show the...     

Implicit and Explicit Model-Based Signal Processing for the Analysis of Short-Term Cardiovascular Interactions

The paper stresses the importance of model-based signal processing in the analysis of the cardiovascular regulation mechanisms. It is remarked that even traditional signal processing implicitly assumes a model and interprets data according to it. Therefore, traditional signal processing is here referred to as implicit model-based signal processing in contrast with explicit model-based signal processing directly stemming from modeling considerations. The paper points out the advantages that can be achieved by rendering explicit the underlying model structure when it is implicit and by jointly applying implicit and explicit model-based signal processing approaches when dealing with complex, interacting,closed-loop biomedical systems. These advantages are rendered practical over several examples derived from the study of the short-term cardiovascular regulation performed by the autonomic nervous system.     

A Time-Domain Based Approach for Short-Term Fading Depth Evaluation in Wideband Mobile Communication Systems

In this paper a time-domain technique for wideband fading depth evaluation, is proposed, which is applicable to both Line-of-Sight and Non-Line-of-Sight cases, and the probability functions of the wideband received power are derived, contributing for filling in the gap of short-term fading characterisation in wideband systems. Application examples for different systems, namely GSM, UMTS and HIPERLAN/2, working in different standard reference environments, are shown. As expected, it is observed that Rayleigh and Rice distributions are appropriate for evaluating the fading margins for GSM in some environments; nevertheless, for UMTS and HIPERLAN/2, fading margins are usually well below the ones obtained from considering these narrowband distributions, the differences reaching up to around 10 and 13 dB, respectively. A simple relationship between the physical and the geometrical environment properties, and the rms delay spread of the propagation channel is also proposed, establishing a relationship between two models independently derived by different authors. Using the proposed relationship, fading depth results from the proposed time-domain technique are compared with the ones for an environment-geometry based one, and a good agreement is verified. The difference in fading depth between both approaches is roughly below 2 dB. Filipe D. Cardoso received the Licenciado, M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from IST/Technical University of Lisbon in 1991, 1996 and 2004, respectively. Since 1994 he has been with the Department of Electrical Engineering of EST/Polytechnic Institute of Setúbal, Setúbal, Portugal, where he is an Adjunct Professor in the Telecommunications area. He is also a researcher at the Telecommunications Institute, Lisbon. He has been a member of COST 273 and IST/FLOWS European projects and is involved in the Network of Excellence in Wireless Communications (NEWCOM). He has authored papers in national and international conferences and journals, for which he has also served as a reviewer. His research interests include wireless/mobile channel characterisation and modelling and future Mobile Broadband Systems. Luis M. Correia received the Ph.D. in electrical and computer engineering from IST/Technical University of Lisbon in 1991, where he is currently a professor in telecommunications, with a focus on wireless/mobile communications in the areas of channel characterisation, traffic, services, and cellular planning. He as acted as a consultant for Portuguese GSM operators and for the telecommunications regulator. Besides being responsible for research projects at the national level, he was or is part of various ones within European frameworks (RACE, ACTS, and IST). He participated in and was co-editor of the Final Report for COST 231, Chairman and editor of the Final Report for COST 259, and is currently Chairman of COST 273. He was and is responsible for the supervision of students at both the M.Sc. and Ph.D. levels. He has authored papers and communications in international journals and conferences, for which he has also served as a reviewer, has been a co-editor of a special issue of a journal on wireless broadband systems, and is currently the editor for Wireless Systems in the European Transactions on Telecommunications. He as served as evaluator and auditor in ACTS, ESPRIT and IST frameworks. He was Chairman of the Technical Program Committee of PIMRC 2002.     

Long Short-Term Memory Spiking Neural Networks for Classification of Snoring and Non-snoring Sound Events

Snoring is a widespreadoccurrence that impacts human sleep quality.It is also one of the earliest symptoms of many sleep disorders.Snoring is accurately detected,making further screening and diagnosis of sleep problems easier.Snoring is frequently ignored because of its underrated and costly detection costs.As a result,this research offered an alternative method for snoring detection based on a long short-term memory based spiking neural network(LSTM-SNN)that is appropriate for large-scale home detection for snoring.We designed acquisition equipment to collect the sleep recordings of 54 subjects and constructed the sleep sound database in the home environment.And Mel frequency cepstral coefficients(MFCCs)were extracted from these sound signals and encoded into spike trains by a threshold encoding approach.They were classified automatically as non-snoring or snoring sounds by our LSTM-SNN model.We used the backpropagation algorithm based on an alternative gradient in the LSTM-SNN to complete the parameter update.The categorization percentage reached an impressive 93.4％,accom-panied by a remarkable 36.9％ reduction in computer power compared to the regular LSTM model.     

高动态短时突发扩频信号的快速捕获技术研究

针对传统伪码捕获结构在处理高动态突发信号时存在的局限性,提出了一种基于载波信号功率谱特性的快速捕获电路结构,通过计算解扩信号功率谱最大值与次大值的比值,并将该比值与判决门限进行比较,从而判断捕获成功与否.采用Welch算法改进信号功率谱的方差特性,计算机仿真结果验证了该算法能够完成高动态环境下短时突发扩频信号伪码的快速捕获.     

An Uplink Short-Term Fading Prediction-Based Power Control Method for DS/CDMA Cellular Radio Systems

In previous research, a new multi-step SIR-based powercontrol, referred to as short-term fadingprediction-based (SFP-based) method, has been proposedfor DS/CDMA downlink power control. Simulation resultsshowed that the SFP method outperforms the originalmulti-step SIR-based power control. In this paper, wefurther investigate the behavior of the SFP methodapplied to uplink. Simulation results show that theSFP method still performs better than the originalmulti-step SIR-based method. Although the uplinkperformance is still poorer than the downlink usingSFP power control method instead of the originalmulti-step SIR-based method, the improvement amount ofSFP-based method compared to the multi-step SIR-basedmethod in uplink is larger than that in downlink.     

全数字接收机中基于准自适应短时反馈的残余频偏纠正算法

本文介绍了一种在全数字接收机中应用的残余频偏纠正算法,此算法通过均方误差准则检测分析有无残余频偏,并规定一个合适的步长值,利用逐次逼近的原理不断反馈以进行残余频偏的纠正,从而有效改善因残余频偏的累积效应而引起的误码率增大问题。仿真结果显示,纠正后系统的误码率显著下降,接收码元的质量得到明显的改善。     

Short-Term Schedulability Analysis of Multiple Distiller Crude Oil Operations in Refinery With Oil Residency Time Constraint

Because of the complexity of a short-term scheduling problem for crude oil operations, some constraints are ignored in modeling the system by the existing approaches, leading to an infeasible solution. To avoid this, the short-term scheduling problem can be studied in control theory perspective by viewing an operation decision in the schedule as a control. With this idea, this paper conducts the schedulability analysis for systems with two and more than two distillers, and the schedulability conditions are presented with the help of Petri net theory. It shows that the number of charging tanks and their capacity, the amount of crude oil of different types in the charging tanks, the oil transportation rate of the pipeline, and the production rate of the system affect the safeness of the system. It also presents the conditions under which the system can reach its maximal production rate. With the safeness conditions and proved results presented in this paper, if a refining schedule is realizable, a feasible detailed schedule for the refining schedule can be easily obtained by creating the operation decisions for the schedule one by one. In the schedule obtained, the starting time of each operation decision can be at any continuous time point, and the schedule is certainly feasible, which overcomes the difficulty faced by techniques that are based on mathematical programming methods.     

A Comparison of the Squared Energy and Teager-Kaiser Operators for Short-Term Energy Estimation in Additive Noise

Time-frequency distributions that evaluate the signal's energy content both in the time and frequency domains are indispensable signal processing tools, especially, for nonstationary signals. Various short-time energy computation schemes are used in practice, including the mean squared amplitude and Teager-Kaiser energy approaches. Herein, we focus primarily on the short- and medium-term properties of these two energy estimation schemes, as well as, on their performance in the presence of additive noise. To facilitate this analysis and generalize the approach, we use a harmonic noise model to approximate the noise component. The error analysis is conducted both in the continuous- and discrete-time domains, deriving similar conclusions. The estimation errors are measured in terms of normalized deviations from the expected signal energy and are shown to greatly depend on both the signals' spectral content and the analysis window length. When medium- and long-term analysis windows are employed, the Teager-Kaiser energy operator is proven superior to the common squared energy operator, provided that the spectral content of the noise is more lowpass than the corresponding signal content, and vice versa. However, for shorter window lengths, the Teager-Kaiser operator always outperforms the squared energy operator. The theoretical results are experimentally verified for synthetic signals. Finally, the performance of the proposed energy operators is evaluated for short-term analysis of noisy speech signals and the implications for speech processing applications are outlined.     

基于改进人工鱼群-蛙跳算法优化LSSVM参数短期负荷预测

短期电力负荷预测在电力系统安全调度、经济运行方面起到关键作用。在用最小二乘支持向量机进行负荷预测时,参数选择将直接影响预测精度。为了提高LSSVM负荷预测精度,文中提出一种基于Levy变异自适应视野人工鱼群-蛙跳算法对LSSVM进行参数优化的方法。以某县负荷、天气等历史数据对LSSVM进行训练,建立LAVAFSA-SFLA-LSSVM、AFSA-LSSVM、LAFSA-SFLA-LSSVM共3种预测模型,对该地区某日24 h的电力负荷进行预测。算例结果表明,LAVAFSA-SFLA-LSSVM预测精度比AFSA-LSSVM和LAFSA-SFLA-LSSVM更高,预测误差更小。     

Absence of Significant Short-Term Electromagnetic Bioeffects in Giant Algal Cells Exposed to CW and Pulse-Modulated X-Band Bursts

Giant cells of the algae Chara brauni and Nitella flexilis were exposed to continuous wave and pulse-modulated bursts of X-band microwaves and the vacuolar potential was monitored for immediate radiation-correlated offsets. No such offsets were observed despite a resolution of approximately 5 in 10/sup 5/, and despite the wide variety of frequencies, power levels, and pulse protocols employed.     

Variations of Thermoelectric and Mechanical Properties of Large Lead Telluride Samples Produced by a Short-Term Sintering Method

Although lead telluride is a widely used thermoelectric (TE) material for generator applications in the intermediate temperature range, its mechanical properties have not been fully understood yet. Especially sintered PbTe samples have hardly been investigated with regard to their mechanical properties and homogeneity in general. The aim of the present study is to comprehend the TE and mechanical properties of large PbTe samples produced by a short-term sintering process. The potential and Seebeck microprobe technique was used to reveal the spatial distribution of the Seebeck coefficient on the samples’ surfaces at room temperature. Microhardness measurements were performed with a Vickers indenter. The results demonstrate that, despite a homogeneous density, the functional and mechanical properties vary along the samples, which is attributed to varying local parameters during sintering.