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This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid "explosion of complexity" and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents' outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Industrial Internet-of-Things systems suffer from a vast and vulnerable attack surface, raising widespread concerns about shielding IIoT devices from malicious attacks and reducing cyber risks. Device identification is the prerequisite to safeguard IIoT systems. We leverage the observation that IIoT network protocol implementations vary due to different hardware architectures/configurations and design tasks of IIoT devices, which cause the difference in their network traffic payloads. Specifically, we develop a novel neural network to learn the semantic/syntax features among multiple IIoT packets. The neural network has multiple branches, each of which consists of convolution layers, attention modules, and highway units for learning the classification model of IIoT devices. To validate the precision and recall of our neural network in IIoT devices fingerprinting, we have implemented a prototype of the proposed IIoT device identification system. Our results show that our approach achieves 95.8% precision and 95.4% recall, significantly outperforming other classification models. • We propose a novel IIoT fingerprinting approach based on neural network. • We conduct experiments to validate the effectiveness of it. • The results show that our approach can achieve 95.8% precision. • We demonstrate the usefulness of fingerprinting in application. • Our approach is useful in shielding industrial systems. [ABSTRACT FROM AUTHOR]

UV absorption is widely used for characterizing proteins structures. The mapping of UV spectra to atomic structure of proteins relies on expensive theoretical simulations, circumventing the heavy computational cost which involves repeated quantummechanical simulations of excited-state properties of many fluctuating protein geometries, which has been a long-time challenge. Here we show that a neural network machine-learning technique can predict electronic absorption spectra of N-methylacetamide (NMA), which is a widely used model system for the peptide bond. Using ground-state geometric parameters and charge information as descriptors, we employed a neural network to predict transition energies, ground-state, and transition dipole moments of many molecular-dynamics conformations at different temperatures, in agreement with time-dependent density-functional theory calculations. The neural network simulations are nearly 3,000× faster than comparable quantum calculations. Machine learning should provide a cost-effective tool for simulating optical properties of proteins. [ABSTRACT FROM AUTHOR]

1. Introduction A generative network is an artificial neural network (ANN) designed to synthesize examples similar in desired ways to the examples on which the network is trained. The examples [...]

In this article, we propose the use of information color to authenticate face; several spaces of colors were used for the transformation of colorimetric components RGB of the original images. The results obtained in different spaces/or component colorimetric are combined by the use of a nonlinear fusion with a simple neuron network type MLP (Multi layer perceptron). We applied the method of linear discriminant analysis (LDA) to extract the characteristic vector of the face image. To validate this work we tested this approach on frontal images of the data base XM2VTS according to its associated protocol (protocol of Lausanne). [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2007.12.004 Byline: Arash Dana (a), Ahmad Khadem Zadeh (b), Seyed Ali Sadat Noori (a) Keywords: Mobile ad hoc networks; Reliability; Routing; Neural networks Abstract: Topological changes in mobile ad hoc networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on quality of service. A suitable technique for eliminating this problem is to use multiple backup paths between the source and the destination in the network. Most of the proposed on-demand routing protocols however, build and rely on single route for each data session. Whenever there is a link disconnection on the active route, the routing protocol must perform a path recovery process. This paper proposes an effective and efficient protocol for backup and disjoint path set in an ad hoc wireless network. This protocol converges into a highly reliable path set very fast with no message exchange overhead. The paths selection according to this algorithm is beneficial for mobile ad hoc networks, since it produces a set of backup paths with much higher reliability. Simulations are conducted to evaluate the performance of our algorithm in terms of route numbers in the path set and its reliability. In order to acquire link reliability estimates, we use link expiration time (LET) between each two nodes. In another experiment, we save the LET of entire links in the ad hoc network during a specific time period, then use them as a data base for predicting the probability of proper operation of links. Links reliability obtains from LET. Prediction is done by using a multi-layer perceptron (MLP) network which is trained with error back-propagation error algorithm. Experimental results show that the MLP net can be a good choice to predict the reliability of the links between the mobile nodes with more accuracy. Author Affiliation: (a) Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Iran (b) Education and International Scientific Cooperation Department, Iran Telecommunication Research Center, Iran Article History: Received 5 September 2006; Accepted 17 December 2007

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Abstract: Building a real-time (RT) control network using standard TCP/IP and Ethernet is often difficult, because the communication protocols usually have unpredictable time delays. When RT packets are transported within an ordinary Ethernet network, there may be a significant time delay before receiving them. This sometimes causes loop instability and performance degradation. A compensation scheme that consists of a fuzzy-PID controller and a neural network compensator is proposed here. This diminishes the influence from time delays on stability while maintaining the system performance. The proposed design is experimentally verified in the real-world network environment to show its effectiveness and applicability. [Copyright &y& Elsevier]

In order to protect the intellectual property of neural network, an owner may select a set of trigger samples and their corresponding labels to train a network, and prove the ownership by the trigger set without revealing the inner mechanism and parameters of the network. However, if an attacker is allowed to access the neural network, he can forge a matching relationship between fake trigger samples and fake labels to confuse the ownership. In this paper, we propose a novel neural network watermarking protocol against the forging attack. By introducing one-way hash function, the trigger samples used to prove ownership must form a one-way chain, and their labels are also assigned. By this way, an attacker without the right of network training is impossible to construct a chain of trigger samples or the matching relationship between the trigger samples and the assigned labels. Our experiments show that the proposed protocol can resist the watermark forgery without sacrificing the network performance. [ABSTRACT FROM AUTHOR]

This paper proposes the lightweight autonomous vehicle self-diagnosis (LAVS) using machine learning based on sensors and the internet of things (IoT) gateway. It collects sensor data from in-vehicle sensors and changes the sensor data to sensor messages as it passes through protocol buses. The changed messages are divided into header information, sensor messages, and payloads and they are stored in an address table, a message queue, and a data collection table separately. In sequence, the sensor messages are converted to the message type of the other protocol and the payloads are transferred to an in-vehicle diagnosis module (In-VDM). The LAVS informs the diagnosis result of Cloud or road side unit(RSU) by the internet of vehicles (IoV) and of drivers by Bluetooth. To design the LAVS, the following two modules are needed. First, a multi-protocol integrated gateway module (MIGM) converts sensor messages for communication between two different protocols, transfers the extracted payloads to the In-VDM, and performs IoV to transfer the diagnosis result and payloads to the Cloud through wireless access in vehicular environment(WAVE). Second, the In-VDM uses random forest to diagnose parts of the vehicle, and delivers the results of the random forest as an input to the neural network to diagnose the total condition of the vehicle. Since the In-VDM uses them for self-diagnosis, it can diagnose a vehicle with efficiency. In addition, because the LAVS converts payloads to a WAVE message and uses IoV to transfer the WAVE messages to RSU or the Cloud, it prevents accidents in advance by informing the vehicle condition of drivers rapidly. [ABSTRACT FROM AUTHOR]