Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

An integrated and cooperative architecture for multi-intersection traffic signal control

Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.

A new approach to detecting weak signal in strong noise based on chaos system control

In this paper, a chaos system and proportional differential control are both used to detect the frequency of an unknown signal. In traditional methods the useful signal is obtained through the Duffing equation or other chaotic oscillators. But these methods are too complex because of using a lot of chaos oscillators. In this paper a new method is presented that uses the Rossler equation and proportional differential control to detect a weak signal frequency. Substituting the detected signal frequency into the RSssler equation leads the Rossler phase state to be considerably changed. The chaos state can be controlled through the proportional differential method. Through its phase diagram and spectrum analysis, the unknown frequency is obtained. The simulation results verify that the presented method is feasible and that the detection accuracy is higher than those of other methods.

DEVELOPMENT AND USAGE OF SIGNAL PR OCESSING AND SOUND SITIMULATING SYSTEM CONTROLLED BY COMPUTER

In hearing physiological experiments and clinic tests,we need not only a signal processing system,but also a synchronous sound stimulator’ Most of stimulators we are now using are function generators which are independent to processing units,and can be controlled only by hand. Although some of them have ports through which they can be controlled by computer,but as they are designed for industrial aims,not for hearing research,most of them can’t generate the special waveforms we need. We use the TDT signal processing system and develop a software package have both usage. On the interface of the program we can control the sampling parameters and generate stimulating waveforms’

Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.

Notch signaling controls chondrocyte hypertrophy via indirect regulation of Sox9

RBPjk-dependent Notch signaling regulates both the onset of chondrocyte hypertrophy and the progression to terminal chondrocyte maturation during endochondral ossification. It has been suggested that Notch signaling can regulate Sox9 transcription, although how this occurs at the molecular level in chondrocytes and whether this transcriptional regulation mediates Notch control of chondrocyte hypertrophy and cartilage development is unknown or controversial. Here we have provided conclusive genetic evidence linking RBPjk-dependent Notch signaling to the regulation of Sox9 expression and chondrocyte hypertrophy by examining tissuespecific Rbpjk mutant（Prx1Cre;Rbpjkf/f）, Rbpjk mutant/Sox9 haploinsufficient（Prx1Cre;Rbpjkf/f;Sox9f/1）,and control embryos for alterations in SOX9 expression and chondrocyte hypertrophy during cartilage development. These studies demonstrate that Notch signaling regulates the onset of chondrocyte maturation in a SOX9-dependent manner, while Notch-mediated regulation of terminal chondrocyte maturation likely functions independently of SOX9. Furthermore, our in vitro molecular analyses of the Sox9 promoter and Notch-mediated regulation of Sox9 gene expression in chondrogenic cells identified the ability of Notch to induce Sox9 expression directly in the acute setting, but suppresses Sox9 transcription with prolonged Notch signaling that requires protein synthesis of secondary effectors.

Gα_s signaling controls intramembranous ossification during cranial bone development by regulating both Hedgehog and Wnt/β-catenin signaling

How osteoblast cells are induced is a central question for understanding skeletal formation. Abnormal osteoblast differentiation leads to a broad range of devastating craniofacial diseases. Here we have investigated intramembranous ossification during cranial bone development in mouse models of skeletal genetic diseases that exhibit craniofacial bone defects. The GNAS gene encodes Gαs that transduces GPCR signaling. GNAS activation or loss-of-function mutations in humans cause fibrous dysplasia(FD) or progressive osseous heteroplasia(POH) that shows craniofacial hyperostosis or craniosynostosis, respectively. We find here that, while Hh ligand-dependent Hh signaling is essential for endochondral ossification, it is dispensable for intramembranous ossification, where Gαsregulates Hh signaling in a ligand-independent manner. We further show that Gαscontrols intramembranous ossification by regulating both Hh and Wnt/β-catenin signaling. In addition, Gαsactivation in the developing cranial bone leads to reduced ossification but increased cartilage presence due to reduced cartilage dissolution, not cell fate switch. Small molecule inhibitors of Hh and Wnt signaling can effectively ameliorate cranial bone phenotypes in mice caused by loss or gain of Gnas function mutations, respectively. Our work shows that studies of genetic diseases provide invaluable insights in both pathological bone defects and normal bone development, understanding both leads to better diagnosis and therapeutic treatment of bone diseases.

FL-FN-MOGA Based Traffic Signal Control

In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

How to Deal with Revolutions in Train Control Systems

本文对高速铁路线路基础沉降及差异沉降的主要影响因素进行了分析,提出局部地下水开采是影响线路差异沉降的直接诱因。通过对不同差异沉降量下无砟轨道附加荷载及高速行车过程中车辆、轨道和桥梁动力响应的研究,初步提出了运营期线路基础差异沉降控制标准。

Web Control System for Transcorneal Electric Stimulation Devices

The electrical stimulation systems dedicated to generating unconventional waveforms have been shown to have a positive effect in the treatment of channelopathies, for example, in open-angle glaucoma. However, these signals can be distorted due to different external circumstances, which could lead to counterproductive effects in treatments such as increased intraocular pressure IOP or other effects that are unknown due to poor electrical signaling. In the present work, a web control system capable of communicating with transcorneal electrical stimulation equipment is proposed for the remote control of treatments applied to patients suffering from various ocular channelopathies. As the first phase of this system, it will only focus on treating patients with open-angle glaucoma since this disease is characterized by an increase in IOP and can be immediately measured by an ophthalmologist.

Iterative Learning Control for a Class of Linear Discrete-time Switched Systems

在这份报纸，反复的学习控制(ILC ) 与任意的切换的信号为线性分离时间的交换系统的一个类被考虑。交换系统重复地在有限时间间隔期间被操作，这被假定，然后第一个顺序 P 类型 ILC 计划能被用来完成完美的追踪在上自始至终间隔。由超级向量途径，为在重复领域的如此的 ILC 系统的一个集中条件能被给。理论分析被模拟支持。

Direct Digital Frequency Control Based on the Phase Step Change Characteristic between Signals

We present a new digital phase lock technology to achieve the frequency control and transformation through high precision multi-cycle group synchronization between signals without the frequency transformation circuit. In the case of digital sampling, the passing zero point of the phase of the controlled signal has the phase step characteristic, the phase step of the passing zero point is monotonic continuous with high resolution in the phase lock process, and using the border effect of digital fuzzy area, the gate can synchronize with the two signals, the quantization error is reduced. This technique is quite different from the existing methods of frequency transformation and frequency synthesis, the phase change characteristic between the periodic signals with different nominal is used. The phase change has the periodic phenomenon, and it has the high resolution step value. With the application of the physical law, the noise is reduced because of simplifying frequency transformation circuits, and the phase is locked with high precision. The regular phase change between frequency signals is only used for frequency measurement, and the change has evident randomness, but this randomness is greatly reduced in frequency control, and the certainty of the process result is clear. The experiment shows that the short term frequency stability can reach 10-12/s orders of magnitude.

人工智能在信号控制系统的应用

随着现代社会科学技术水平不断提高,人工智能技术在许多领域都有了深入广泛的应用。大城市交通拥堵问题日益加剧,对信号控制智能化水平提出更高的要求。通过分析上海目前信号控制系统存在的问题,提出将人工智能技术应用到信号控制系统的设想,并对应用场景进行展望。智能信号控制系统是提高通行效率、缓解交通拥堵的重要抓手,是未来提升城市精细化交通管理水平的重要发展方向。

机械工程背景下测控专业信号与系统课程教学效果提升策略探索

信号与系统在测控技术与仪器专业人才培养方案中占有重要的地位,在教学过程中需要进行不断的改革以适应人才培养的需求。针对于机械工程背景和学时少的特点,提出总结过往教学效果、教与学并用攻克难点、紧密结合科研或实际工程、课程思政元素的有机融入等提升教学效果的策略,这些策略在一定程度上能够帮助学生克服对新知识的排斥心理和激发求知的主观能动性,有效地提高了课程目标达成度,对于提升人才培养质量具有积极的作用。

Hardware Implementation and System Control Strategy of Videoconference System Based on Rec. H. 261

The design and realization of a videoconference system based on international recommendation are introduced in this paper, and the hardware implementation of video codec based on ITU-T H. 261 is briefly discussed. Furthermore, the buffer control method and the adaptive control strategy for quantization are proposed, which are adaptive and robust. This system can be operated under the transmission rate ranging from 128kb/s to 2Mb/s. With these strategies for the videoconference system, the high quality image is obtained. The time delay of the system is about half a second.

Adaptive Learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion Control of an Industrial Robot

Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.

具有介质访问约束的网络化控制系统H∞状态估计

研究了一类存在介质访问约束和信号量化的网络化控制系统的H∞状态估计问题。首先,考虑到介质访问约束和信号量化的影响,将信号量化误差转化为扇形有界的不确定性,对由于访问约束而未获得网络传输的系统输出采用加权补偿策略,根据网络介质的随机访问机制将网络化系统建模为具有不确定性的马尔可夫跳跃系统。然后,在此基础上借助李雅普诺夫稳定性理论和H∞性能约束条件,利用线性矩阵不等式(linear matrix inequality,LMI)方法给出状态估计器存在的充分条件,所设计的状态估计器使得估计误差系统渐近稳定且具有给定的H∞性能。最后,通过仿真实例验证了该设计方法的有效性。