Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

N-SOLITON (N>2) INTERACTION IN THE PRESENCE OF THIRD-ORDER DISPERSION OF OPTICAL FIBERS

N-soliton (N>2) interaction in optical fibers including third-order dispersion is simulated numerically.In equal amplitude soliton transmission,the coalescence resulted from the mutual interaction among N solitons still exists,while in the unequal case,soliton interaction can not be removed but becomes more serious due to the effect of third-order dispersion.Therefore,we can not consider third-order dispersion as an utilizable factor for removing soliton interaction.

Integrated Sliding Mode Velocity Control of Linear Permanent Magnet Synchronous Motor with Thrust Ripple Compensation

In this paper,a compound sliding mode velocity control scheme with a new exponential reaching law(NERL)with thrust ripple observation strategy is proposed to obtain a high performance velocity loop of the linear permanent magnet synchronous motor(LPMSM)control system.A sliding mode velocity controller based on NERL is firstly discussed to restrain chattering of the conventional exponential reaching law(CERL).Furthermore,the unavoidable thrust ripple caused by the special structure of linear motor will bring about velocity fluctuation and reduced control performance.Thus,a thrust ripple compensation strategy on the basis of extend Kalman filter(EKF)theory is proposed.The estimated thrust ripple will be introduced into the sliding mode velocity controller to optimize the control accuracy and robustness.The effectiveness of the proposal is validated with experimental results.

Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation

Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.

A Deep Learning Based Broadcast Approach for Image Semantic Communication over Fading Channels

We consider an image semantic communication system in a time-varying fading Gaussian MIMO channel,with a finite number of channel states.A deep learning-aided broadcast approach scheme is proposed to benefit the adaptive semantic transmission in terms of different channel states.We combine the classic broadcast approach with the image transformer to implement this adaptive joint source and channel coding(JSCC)scheme.Specifically,we utilize the neural network(NN)to jointly optimize the hierarchical image compression and superposition code mapping within this scheme.The learned transformers and codebooks allow recovering of the image with an adaptive quality and low error rate at the receiver side,in each channel state.The simulation results exhibit our proposed scheme can dynamically adapt the coding to the current channel state and outperform some existing intelligent schemes with the fixed coding block.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

Guest Editorial

IEEE International Conference on Communication Technology(ICCT)was jointly initiated and organized by IEEE Beijing Section and IEEE Com Soc in 1986.It is one of the highest-level academic events in the field of communication technology in China.It is also an international gathering for research for information and communication technology.

A Study on Development of a Hybrid Aerial/Terrestrial Robot System for Avoiding Ground Obstacles by Flight

To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However,less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.

Impact of Fiber Dispersion on Performance of Entanglement-Based Dispersive Optics Quantum Key Distribution

Dispersive optics quantum key distribution(DO-QKD)based on energy-time entangled photon pairs is an important QKD scheme.In DO-QKD,the arrival time of photons is used in key generation and security analysis,which would be greatly affected by fiber dispersion.In this work,we establish a theoretical model of the entanglement-based DO-QKD system,considering the protocol,physical processes(such as fiber transmission and single-photon detection),and the analysis of security tests.Based on this theoretical model,we investigate the influence of chromatic dispersion introduced by transmission fibers on the performance of DO-QKD.By analyzing the benefits and costs of dispersion compensation,the system performance under G.652 and G.655 optical fibers are shown,respectively.The results show that dispersion compensation is unnecessary for DO-QKD systems in campus networks and even metro networks.Whereas,it is still required in DO-QKD systems with longer fiber transmission distances.

A Study on Hovering Control of Small Aerial Robot by Sensing Existing Floor Features

Since precise self-position estimation is required for autonomous flight of aerial robots, there has been some studies on self-position estimation of indoor aerial robots. In this study, we tackle the self-position estimation problem by mounting a small downward-facing camera on the chassis of an aerial robot. We obtain robot position by sensing the features on the indoor floor.In this work, we used the vertex points(tile corners) where four tiles on a typical tiled floor connected, as an existing feature of the floor. Furthermore, a small lightweight microcontroller is mounted on the robot to perform image processing for the onboard camera. A lightweight image processing algorithm is developed. So, the real-time image processing could be performed by the microcontroller alone which leads to conduct on-board real time tile corner detection. Furthermore, same microcontroller performs control value calculation for flight commanding. The flight commands are implemented based on the detected tile corner information. The above mentioned all devices are mounted on an actual machine, and the effectiveness of the system was investigated.

Analysis and Simulation of a High Performance Quasi Resonant PWM Inverter for Solar Power Generation

A quasi resonant pulse width modulation（PWM） inverter is used in a solar power system to convert the solar panel and battery charger＇s direct current（DC） output to alternating current（AC）.Although much has been published about DC to AC PWM inverters,none of the previous work has shown modeling and simulation results for DC to AC inverters.In this study,we suggest a new topology for a quasi resonant PWM inverter.Experimental results are also presented.

Elimination of Spatial Side-Channel Information for Compact Quantum Key Distribution Senders

For a compact quantum key distribution (QKD) sender for the polarization encoding BB84 protocol, an eavesdropper could take a side-channel attack by measuring the spatial information of photons to infer their polarizations. The possibility of this attack can be reduced by introducing an aperture in the QKD sender, however, the effect of the aperture on the QKD security lacks of quantitative analysis. In this paper, we analyze the mutual information between the actual keys encoded at this QKD sender and the inferred keys at the eavesdropper (Eve), demonstrating the effect of the aperture to eliminate the spatial side-channel information quantitatively. It shows that Eve’s potential on eavesdropping spatial side-channel information is totally dependent on the optical design of the QKD sender, including the source arrangement and the aperture. The height of compact QKD senders with integrated light-emitting diode (LED) arrays could be controlled under several millimeters, showing great potential on applications in portable equipment.

Design of Power Switching and Distribution Circuits for Solar Power Generation

Abstract--With the development of clean energy, switching and distribution issues in a photovoltaic system are getting much attention in recent years. This paper designs a DC to AC inverter and power switching and distribution system between a solar power system and the municipal system by using the Darlington amplifier structure with the photosensitive resistor and accompanying relays, and details the system circuits. The proposed system can achieve a stable output of IIOV AC, as well as self-generating driving voltage and switching between the municipal electrical system and the solar power system. The mathematic analysis and actually test results demonstrate that the proposed method is an easy, inexpensive, and low cost way to build a solar power switching and distribution system.

MEASUREMENTS AND APPLICATIONS OF QoE FOR MULTIMEDIA COMMUNICATIONS

With the rapid development of smart devices and mobile networks,multimedia services will dominate most of data traffic in 4G/5G networks.Applications -such as conversational videos,online multimedia sharing,remote education,etc.have gained their popularity and will become more ubiquitous among customers.Tra-

Application of Internet of Things Micropower Smart Switch in Temperature Monitoring

This study uses the smart phone with the Android system to construct a cloud-side smart switch system in the client-server architecture with the Android open platform system, using microprocessors(MCU 16F690) as the thermostat controller and combined with Raspberry Pi. The computing technology extends the control of the constant temperature from the local to the cloud, allowing the user to view the temperature status recorded by the cloud server using the smart phone application(App) or web browser. It can even remotely control the heating power of the smart switch for the heating device in the Internet control environment and use the proportional-integral-derivative(PID) control and the pulse-width modulation(PWM) technology to achieve intelligent constant temperature control.The proposed control system uses the expert PID control as the core to calculate the duty cycle of the PWM signal to control the power output of the smart switch for the constant temperature water tank. The experiment results verify the effectiveness of the proposed system.

Codebook Design and Beam Training for Extremely Large-Scale RIS:Far-Field or Near-Field?

Reconfigurable intelligent surface(RIS)is more likely to develop into extremely large-scale RIS(XL-RIS)to efficiently boost the system capacity for future 6 G communications.Beam training is an effective way to acquire channel state information(CSI)for XL-RIS.Existing beam training schemes rely on the far-field codebook.However,due to the large aperture of XL-RIS,the scatters are more likely to be in the near-field region of XL-RIS.The far-field codebook mismatches the near-field channel model.Thus,the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted nearfield communications.To solve this problem,we propose the efficient near-field beam training schemes by designing the near-field codebook to match the nearfield channel model.Specifically,we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS.Then,the optimal codeword for XL-RIS is obtained by the exhausted training procedure.To reduce the beam training overhead,we further design a hierarchical nearfield codebook and propose the corresponding hierarchical near-field beam training scheme,where different levels of sub-codebooks are searched in turn with reduced codebook size.Simulation results show the proposed near-field beam training schemes outperform the existing far-field beam training scheme.

Wideband Channel Estimation for THz Massive MIMO

Terahertz(THz)communication is considered to be a promising technology for future 6G network.To overcome the severe attenuation and relieve the high power consumption,massive multipleinput multiple-output(MIMO)with hybrid precoding has been widely considered for THz communication.However,accurate wideband channel estimation,which is essential for hybrid precoding,is challenging in THz massive MIMO systems.The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect.In this paper,we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation in THz massive MIMO systems.Specifically,a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect.Based on the analysis,we define a series of index sets called as beam split patterns,which are proved to have a one-to-one match to different physical channel directions.Inspired by this one-to-one match,we propose to estimate the physical channel direction by exploiting beam split patterns at first.Then,the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window.This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction.The above estimation procedure will be repeated path by path until all path components are estimated.Finally,the wideband channel can be recovered by calculating the elements on the total sparse channel support at all subcarriers.The proposed scheme exploits the wideband channel property implied by the beam split effect,i.e.,beam split pattern,which can significantly improve the channel estimation accuracy.Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.

An iterative re-weighted least-squares algorithm for the design of active absorbing wavemaker controller

In physical model tests for highly reflective structures, one often encounters a problem of multiple reflections between the reflective structures and the wavemaker. Absorbing wavemakers can cancel the re-reflective waves by adjusting the paddle motion. In this paper, we propose a method to design the controller of the 2-D absorbing wavemaker system in the wave flume. Based on the first-order wavemaker theory, a frequency domain absorption transfer function is derived. Its time realization can be obtained by de- signing an infinite impulse response （IIR） digital filter, which is expected to approximate the absorption transfer function in the least- squares sense. A commonly used approach to determine the parameters of the IIR filter is applying the Taylor expansion to linearize the filter formulation and solving the linear least-squares problem. However, the result is not optimal because the linearization cha- nges the original objective function. To improve the approximation performance, we propose an iterative reweighted least-squares （IRLS） algorithm and demonstrate that with the filters designed by this algorithm, the approximation errors can be reduced. Physical experiments are carried out with the designed controller. The results show that the system performs well for both regular and irregu- lar waves.

From electromagnetic bandgap to left-handed metamaterials:modelling and applications

In this paper, numerical modelling of left-handed materials （LHMs） is presented using in-house and commercial software packages. Approaches used include the finite-difference time-domain （FDTD） method, finite element method （FEM） and method of moments （MoMs）. Numerical simulation includes verification of negative refraction and ＂perfect lenses＂ construction, investigation of evanescent wave behaviour in layered LHMs, reversed Shell＇s Law in electromagnetic band gap （EBG）-like structures and construction of LHMs using modified split ring resonators （SRRs）. Numerical results were verified to be in good agreement with theory, At the end of this paper, potential applications of LHMs in microwave engineering are discussed.

Finite Control Set Model Predictive Torque Control Using Sliding Model Control for Induction Motors

Finite control set model predictive torque control(FCS-MPTC)has become increasingly prevalent for induction motors(IM)owing to its simple concept,easy incorporation of constraints and strong flexibility.In traditional FCS-MPTC speed controller design,a classical proportional integral(PI)controller is typically chosen to generate the torque reference.However,the PI controller is dependent on system parameters and sensitive to the load torque variation,which seriously affects control performance.In this paper,a model predictive torque control using sliding mode control(MPTC+SMC)for IM is proposed to enhance the robust performance of the drive system.First,the influence of the parameter mismatches for FCS-MPTC is analyzed.Second,the shortcomings of traditional PI controller are derived.Then,the proposed MPTC+SMC method is designed,and the MPTC+PI and MPTC+SMC are compared theoretically.Finally,experimental results demonstrate the correctness and effectiveness of the proposed MPTC+SMC.In comparison with MPTC+PI,MPTC+SMC has the better dynamic performance and stronger robust performance against parameter variations and load disturbance.