The Discussion of the Results of an Experimental Study on Meta-Cognitive Strategy with the Perspective of Error Analysis in Listening Training

Though abundant work has been done in both meta-cognitive strategy and error analysis, very little has been done in the exact execution model of applying meta-cognitive strategy with the perspective of error analysis. This study, presuming the construction of an integrative execution model of meta-cognitive strategy with the perspective view of error analysis, focuses on the results and discussion.

APPLICATION OF HUMAN COGNITIVE MODEL FOR TIME DEPENDENT OPERATOR BEHAVIOR IN CHINESE NPP

This paper discusses some issues on human reliability model of time dependent human behavior. Some results of the crew reliability experiment on Tsinghua training simulator in China are given, Meanwhile, a case of calculation for human error probability during anticipated transient without scram (ATWS) based on the data drew from the recent experiment is offered.

Adaptive Spectr um Decision Method for Heterogeneous Cognitive Radio Networks

To improve spectrum utilization and minimize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Secondary User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum decision, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the interrupted service of SU as newly incoming and re-decision process is initialized to find available spectrum in a First-Available-First-Access (FAFA) fashion. Effect of sensing error in PHY layer is also analyzed in terms of extended average residual time. Simulation results show that, for relatively low arriving rate of SU traffic, the proposed spectrum decision method yields at least a delay reduction of 39.5% compared with non-adaptive method. The proposed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.

Joint channel and power allocation scheme for cognitive wireless networks

Cognitive radio （CR） is a promising technology deemed to improve the efficiency of spectrum utilization. This paper considers a spectrum underlay cognitive radio network, in which the cognitive users （CUs） are allowed to use the radio spectrum concurrently with the primary users （PUs） under the interference temperature constraint. We investigate the system performance by using the proposed joint channel and power allocation scheme under two transmit strategies to achieve higher data rates and performance diversity gain respectively. Simulation results show that the proposed scheme provides a significant improvement on the bit error rate （BER） performance and spectrum efficiency of a cognitive wireless network.

A LOW EVM ZERO-IF RF TRANSMITTER FOR COGNITIVE RADIO APPLICATION

A zero-IF transmitter for Cognitive Radio(CR) application is presented.To effectively reduce the interference between Power Amplifier(PA) and Voltage Controlled Oscillator(VCO),two VCOs are adopted,one is 450 MHz and the other is from 1148 MHz to 1252 MHz with an 8 MHz step,so the frequency of them are different from the operational frequency of PA.The Local Oscillator(LO) of the modulator generated by mixing the signals of the two VCOs has a low phase noise of-82 dBc/Hz with an offset of 1 kHz.The measurement result of the transmitter shows that the Adjacent Channel Power Ratio(ACPR) is less than-47.5 dBc at 27 dBm output,and the Error Vector Magnitude(EVM) is less than 1.7%.

EFFICIENT DESIGN OF ANTI-JAMMING RATELESS CODING IN COGNITIVE OFDM

Efficient anti-jamming rateless coding based on cognitive Orthogonal Frequency Division Multiplexing (OFDM) modulation in Cognitive Radio Network (CRN) is mainly discussed. Rateless coding with small redundancy and low complexity is presented, and the optimal design methods of building rateless codes are also proposed. In CRN, anti-jamming rateless coding could recover the lost packets in parallel channels of cognitive OFDM, thus it protects Secondary Users (SUs) from the in-terference by Primary Users (PUs) efficiently. Frame Error Rate (FER) and throughput performance of SU employing anti-jamming rateless coding are analyzed in detail. Performance comparison between rateless coding and piecewise coding are also presented. It is shown that, anti-jamming rateless coding provides low FER and Word Error Rate (WER) performance with uniform sub-channel selection. Meanwhile, it is also verified that, in higher jamming rate and longer code redundancy scenario, rateless coding method could achieve better FER and throughput performance than another anti-jamming coding schemes.

Throughput optimization for multi-channel cooperative CR under reporting channel errors

To overcome the problem of channel fading and noise uncertainty,cooperative spectrum sensing(CSS)is developed to enhance the sensing performance in cognitive radio networks(CRNs).Considering that the non-ideal reporting channels of CSS can cause an adverse impact on the detection performance,the throughput maximization problem for multi-channel CSS cognitive radio under reporting channel errors is investigated.While providing all the primary users with sufficient protection,the average throughput of secondary users(SUs)is maximized by jointly optimizing the sensing duration,detection threshold and SU assignment.To address the non-convex optimization problem,the optimal energy detection threshold is derived first.Then,a sub-optimal greedy algorithm is proposed to obtain the optimal sensing duration and the optimal SU assignment.Analysis and simulation results show that the proposed algorithm can output the same performance as the exhaustive search algorithm at a much lower level of complexity.It is also shown that the impact of imperfect reporting channels should be considered especially in low signal-to-noise ratio environments and the reporting channel errors significantly reduce the performance of CSS.

Handling Uncertainty in Human Cognitive Reliability Method for Safety Assessment Based on DSET

Human Reliability Analysis(HRA)is an important part in safety assessment of a large complex system.Human Cognitive Reliability(HCR)model is a method of evaluating the probability that operators fail to complete during diagnostic decision making within a limited time,which is widely used in HRA.In the application of this method,cognitive patterns of humans are required to be considered and classified,and this process often relies on the evaluation opinions of experts which is highly subjective and uncertain.How to effectively express and process this uncertain and subjective information plays a critical role in improving the accuracy and applicability of HCR.In this paper,a new model was proposed to deal with the uncertain information which exists in the processes of cognitive pattern classification in HCR.First,an evaluation panel was constructed based on expert opinions and processing including setting corresponding anchor points and qualitative indicators of different cognitive patterns,and mapping them to fuzzy numbers and unit intervals.Second,based on the evaluation panel,different analysts judge the cognitive pattern types of actual specific events and provide the level of confidence he or she has in the judgments.Finally,the evaluation opinions of multiple analysts were expressed and fused based on the Dempster-Shafer Evidence Theory(DSET),and the fused results were applied to the HCR model to obtain the Human Error Probability(HEP).A case study was used to demonstrate the procedure and effectiveness of the proposed method.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

A relay selection scheme based on geometric optimum in cooperative cognitive systems over mixed channels

This paper proposes a relay selection scheme based on geometric optimum principle to maximize the cognitive link＇ s connectivity with limited interference to primary user in cooperative cognitive systems. A dual-hop cognitive relay system is considered, in which the channel impulse response follows independent non-identical distribution （i. n. d. ） each hop, such as Rician, Rayleigh and Nakagami-m distribution. Then, closed-form expressions in terms of outage probability and average bit error rate （ABER） are obtained using amplify-and-forward （AF） relaying protocol over such mixed fading channels. Furthermore, the best range of the relay is derived. Extensive simulation re- suits are conducted to verify the theoretical analysis, which is useful to the network optimal design.

Towards cognitive navigation:A biologically inspired calibration mechanism for the head direction cell network

To derive meaningful navigation strategies,animals have to estimate their directional headings in the environment.Accordingly,this function is achieved by the head direction cells that were found in mammalian brains,whose neural activities encode one’s heading direction.It is believed that such head direction information is generated by integrating self-motion cues,which also introduces accumulative errors in the long term.To eliminate such errors,this paper presents an efficient calibration model that mimics the animals’behavior by exploiting visual cues in a biologically plausible way,and then implements it in robotic navigation tasks.The proposed calibration model allows the agent to associate its head direction and the perceived egocentric direction of a visual cue with its position and orientation,and therefore to calibrate the head direction when the same cue is viewed again.We examine the proposed head direction calibration model in extensive simulations and real-world experiments and demonstrate its excellent performance in terms of quick association of information to proximal or distal cues as well as accuracy of calibrating the integration errors of the head direction.Videos can be viewed at https://videoviewsite.wixsite.com/hdc-calibration.

Linear MMSE Channel Estimation for Underlay Cognitive Radio Networks

Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-band transmission including pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on imperfect channel estimation are described. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome problems while enhancing the MIMO communication performance.

Theoretical Approaches to Error Analysis

Contrast Analysis (CA), Interlanguage(IL),cognitive approach are considered as three aspects closely related to Error Analysis (EA).Originated from CA, EA takes IL as its linguistic basis and cognitive approach as its psychological support.Comparing with CA, EA pays more attention to the learner himself rather than the linguistic forms, and error is therefore shifted from what should be avoided to the crucial approach to the exploration of the learner’s cognitive process.

Blind recognition of k/n rate convolutional encoders from noisy observation

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method. © 2017 Beijing Institute of Aerospace Information.

Human Factor Based Leadership: Critical Leadership Tools to Reduce Burnout and Latent Error in a Time of Accelerating Change

The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advances in technology create new levels of stress and expectations on healthcare workers (HCW) with an endless infusion of requirements from multiple authoritative sources that are tracked and monitored. The quality of care and safety of patients is affected by the wellbeing of HCWs who now practice in an environment that has become more complex to navigate, often expending limited neural resource (brainpower) on classifying, organizing, constantly making decisions on how and when they can accomplish what is required(extraneous cognitive load) in addition to direct patient care. New information demonstrates profound biological impact on the brains of those who have burnout in areas that affect the quality and safety of the decisions they make-which affects risk to patients in healthcare. Healthcare administration curriculum currently does not include ways to address these stress-induced problems in healthcare delivery. The science of human factors and ergonomics (HFE) promotes system performance and worker wellbeing. Patient safety is one component of system performance. Since many requirements come without resource to accomplish them, it becomes incumbent upon health system leadership to organize the means for completion of these to minimize the needless loss of brain power diverted away from the delivery of patient care. Human Factor-Based Leadership (HFBL) is an interactive, problem solving seminar series designed for healthcare leaders. The purpose is to provide relevant human factor science to integrate into their leadership and management decisions to make HCWs occupational environment more manageable and sustainable-which makes safer conditions for clinician wellbeing and patient care. After learning the content, a cohort of healthcare leaders believed that adequately addressing HFE in healthcare delivery would significantly reduce clinician burnout and risk of latent errors from upstream leadership decisions. An overview of the content of the seminars is described. Leadership feedback on usability of these seminars is reported. Three HFBL seminars described are Human Factor Relevance in Leadership, Biopsychosocial Approach to Wellness and Burnout, Human Factor Based Leadership: Examples and Applications.

An Evaluation of Human Error Probabilities for Critical Failures in Auxiliary Systems of Marine Diesel Engines

Human error,an important factor,may lead to serious results in various operational fields.The human factor plays a critical role in the risks and hazards of the maritime industry.A ship can achieve safe navigation when all operations in the engine room are conducted vigilantly.This paper presents a systematic evaluation of 20 failures in auxiliary systems of marine diesel engines that may be caused by human error.The Cognitive Reliability Error Analysis Method(CREAM)is used to determine the potentiality of human errors in the failures implied thanks to the answers of experts.Using this method,the probabilities of human error on failures were evaluated and the critical ones were emphasized.The measures to be taken for these results will make significant contributions not only to the seafarers but also to the ship owners.

改进贝叶斯网络模型在起重作业人机交互差错风险分析中的应用

为量化分析起重作业人机交互差错风险,根据安全工效学原理及安全技术规范将起重作业人、机、环相关影响因素作为根节点,按照事故致因层次关联关系确定子节点,构建起重作业人机交互差错的3层级贝叶斯网络模型(Bayesian Network, BN);基于模糊集理论,采用认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM),厘定贝叶斯网络父节点失效概率以及中间节点条件概率;利用逆向推理仿真技术分析起重作业人机交互差错发生的因果链,探究起重伤害事故发生的人机交互差错风险。结果表明:起重作业人机交互差错最可能致因链为起重设备安全检查不到位→管理人员失误→人员操作失误→起重伤害事故发生;单因素失效条件下,起重作业人机交互差错风险概率呈线性增长趋势;在多因素失效条件下,一级节点因素失效概率愈大则人机交互差错效应愈显著,且呈现非线性增长态势。

“数形结合”——测量及不确定度教学模式的改革与研究

理解测量的不确定度是大学物理实验课程一项重要的教学目标。在大学物理实验绪论课中,准备了两个版本的不确定性教学方式:即有无测量数据的图形化展示。课程结束后,对学生进行测试,研究数据的图形化展示是否能够提高学生对不确定度的理解。结果表明,有图形展示的班级学生比没有图形展示的班级学生得分更高。图形表示有助于数据的可视化,在进行测量数据分析时减少了学生的认知负荷,因此应该在教学中积极推广使用。

基于硬件损伤的认知反向散射通信网络鲁棒安全资源分配算法

为了提高反向散射通信网络频谱效率、传输鲁棒性及信息安全性,该文提出一种基于硬件损伤的认知反向散射通信网络鲁棒安全资源分配算法。首先,考虑认知反向散射用户的最小安全速率、传输时间、能量收集和反射系数等约束,基于有界信道不确定性和频谱感知误差模型,建立一个多变量耦合的吞吐量最大化非凸资源分配问题。其次,利用最坏准则、连续凸近似和交替优化方法,将原问题转换为凸优化问题,并提出一种基于迭代的鲁棒资源分配算法。仿真结果表明,与现有算法对比,所提算法具有较好的鲁棒性。

差错报告的前因机制与理论评述

差错报告行为指的是员工通过口头方式或者正式的差错报告系统向上级领导揭示工作差错相关议题的一种工作行为。该行为是组织甄别、预防和管理组织差错的有效方式,对于防范组织失败,降低差错损害,提高组织学习和创新具有重要的意义。本文主要集中探讨差错报告的前因机制,从社会学习、认知、情绪以及组织系统等四种主要的理论视角进行分析,提出未来研究应着重于探讨差错报告行为的理论建构与整合、测量开发以及多层分析与情境交互。