We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RTIoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & controlling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.
During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterpriselevel scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for scheduling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.
We present a new framework for cognitive maintenance (CM) based on cyber-physical systems and advanced artificial intelligence techniques. These CM systems integrate intelligent deep learning approaches and intelligent decision-making techniques, which can be used by maintenance professionals who are working with cutting-edge equipment. The systems will provide technical solutions to real-time online maintenance tasks, avoid outages due to equipment failures, and ensure the continuous and healthy operation of equipment and manufacturing assets. The implementation framework of CM consists of four modules, i.e., cyber-physical system, Internet of Things, data mining, and Internet of Services. In the data mining module, fault diagnosis and prediction are realized by deep learning methods. In the case study, the backlash error of cutting-edge machine tools is taken as an example. We use a deep belief network to predict the backlash of the machine tool, so as to predict the possible failure of the machine tool, and realize the strategy of CM. Through the case study, we discuss the significance of implementing CM for cuttingedge equipment, and the framework of CM implementation has been verified. Some CM system applications in manufacturing enterprises are summarized.
Constant current power transmission is considered a good choice for subsea observatories due to its high resistance to shunt faults. A constant current subsea observatory is planned to be constructed in the East China Sea. We discuss a constant current subsea observatory system used for scientific experiments. The power system and its heat dissipation system are carefully designed. The power conversion method is challenging due to the use of constant current power, which is considerably different from traditional power systems. Thus, we adopt power compensation circuits in the conversion system to obtain a constant 48-V output for science users. A power management system that performs overvoltage protection and real-time monitoring and control of junction box is discussed. An innovative heat dissipation structure of a junction box is designed in consideration of a sealed working environment to extend the useful life of the junction box. Simulations and experiments reveal that the system has high efficiency and stability, especially in long-term applications.
An ammonia self-managed vaporization propulsion (ASVP) system for micro-nano satellites is presented. Compared with a normal cold gas or liquefied gas propulsion system, a multiplex parallel sieve type vaporizer and related vaporization control methods are put forward to achieve self-managed vaporization of liquefied propellant. The problems of high vaporization latent heat and incomplete vaporization of liquefied ammonia are solved, so that the ASVP system takes great advantage of high theoretical specific impulse and high propellant storage density. Furthermore, the ASVP operation procedure and its physical chemistry theories and mathematical models are thoroughly analyzed. An optimal strategy of thrust control is proposed with consideration of thrust performance and energy efficiency. The ground tests indicate that the ASVP system weighs 1.8 kg (with 0.34-kg liquefied ammonia propellant) and reaches a specific impulse of more than 100 s, while the power consumption is less than 10 W. The ASVP system meets multiple requirements including high specific impulse, low power consumption, easy fabrication, and uniform adjustable thrust output, and thus is suitable for micro-nano satellites.
We propose a contraction transformation algorithm to plan a complete coverage trajectory for a mobile robot to accomplish specific types of missions based on the Arnold dynamical system. First, we construct a chaotic mobile robot by combining the variable z of the Arnold equation and the kinematic equation of the robot. Second, we construct the candidate sets including the initial points with a relatively high coverage rate of the constructed mobile robot. Then the trajectory is contracted to the current position of the robot based on the designed contraction transformation strategy, to form a continuous complete coverage trajectory to execute the specific types of missions. Compared with the traditional method, the designed algorithm requires no obstacle avoidance to the boundary of the given workplace, possesses a high coverage rate, and keeps the chaotic characteristics of the produced coverage trajectory relatively unchanged, which enables the robot to accomplish special missions with features of completeness, randomness, or unpredictability.
Footwear prints are important evidence in criminal investigation. They represent changes in the surface morphology due to disturbance to fine particle distributions. Existing non-contact optical detection methods usually measure the light intensity contrasts between the footwear prints and the ground, which can be enhanced by grazing incident illumination. We take polarization images of footwear prints on different types of floors using a commercial single lens reflex color camera. Results show that adding linear polarizers in front of the camera lens and light source improves the contrast of footwear print images. The best contrasts are achieved in degree of linear polarization. In addition, the three-color channels of the camera can be used to examine the spectral features of the polarization images. According to the experimental results, the best contrast is obtained at the blue channel. The current work shows that grazing incidence polarized light imaging can effectively enhance the contrast of the footwear prints against the floors, which would help obtain footwear evidence in criminal investigation.
Although concern has been recently expressed with regard to the solution to the non-convex problem, convex optimization is still important in machine learning, especially when the situation requires an interpretable model. Solution to the convex problem is a global minimum, and the final model can be explained mathematically. Typically, the convex problem is re-casted as a regularized risk minimization problem to prevent overfitting. The cutting plane method (CPM) is one of the best solvers for the convex problem, irrespective of whether the objective function is differentiable or not. However, CPM and its variants fail to adequately address large-scale dataintensive cases because these algorithms access the entire dataset in each iteration, which substantially increases the computational burden and memory cost. To alleviate this problem, we propose a novel algorithm named the mini-batch cutting plane method (MBCPM), which iterates with estimated cutting planes calculated on a small batch of sampled data and is capable of handling large-scale problems. Furthermore, the proposed MBCPM adopts a “sink” operation that detects and adjusts noisy estimations to guarantee convergence. Numerical experiments on extensive real-world datasets demonstrate the effectiveness of MBCPM, which is superior to the bundle methods for regularized risk minimization as well as popular stochastic gradient descent methods in terms of convergence speed.
To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records, a method for constructing a knowledge graph of power equipment defects is presented. Then, a graph search algorithm is employed to recognize different kinds of errors in defect records, based on the knowledge graph of power equipment defects. Finally, an error recognition example in terms of transformer defect records is given, by comparing the precision, recall, F1-score, accuracy, and efficiency of the proposed method with those of machine learning methods, and the factors influencing the error recognition effects of various methods are analyzed. Results show that the proposed method performs better in error recognition of defect records than machine learning methods, and can satisfy real-time requirements.
Quantum-dot cellular automata (QCA) based on cryptography is a new paradigm in the field of nanotechnology. The overall performance of QCA is high compared to traditional complementary metal-oxide semiconductor (CMOS) technology. To achieve data security during nanocommunication, a cryptography-based application is proposed. The devised circuit encrypts the input data and passes it to an output channel through a nanorouter cum data path selector, where the data is decrypted back to its original form. The results along with theoretical implication prove the accuracy of the circuit. Power dissipation and circuit complexity of the circuit have been analyzed.
Orthogonal frequency division multiplexing (OFDM) has been adopted as standard beginning with the 4th generation mobile communication system because of its high-bit-rate transmission capability under frequency selective fading channel conditions. However, a major disadvantage of OFDM is the non-constant envelope signal with a high peak-to-average power ratio (PAPR). The high peak signal in OFDM is distorted through a nonlinear amplifier, which causes bit error ratio (BER) reduction. Many techniques have been developed for reducing PAPR at the cost of inefficient bandwidth usage or throughput because of the additional information about PAPR reduction. We propose a novel method, in which the high peak signal above the threshold of the nonlinear amplification region is nonlinearly downscaled to lower the PAPR. The time slot location and scaling ratio for where and how the high peak baseband OFDM signal is downscaled are transmitted using frequency modulation (FM) combined with OFDM, which requires less additional bandwidth than the previously proposed methods. Simulation results show that the proposed novel method provides a lower PAPR and elicits a better BER performance compared with other conventional methods, because it reduces the PAPR by nonlinear scaling and restores the pre-distorted signal using FM.