Volume 20, Number 1, 2017
Wenping YU, Jun WANG, Hong PENG, Jun MING, Chengyu TAO, Tao WANG
Abstract. Combining interval-valued fuzzy numbers with spiking neural P systems (SN P systems, in short), an extended SN P system model is developed for fault diagnosis of power systems, called fuzzy reasoning spiking neural P systems with interval-valued fuzzy numbers (ivFRSN P systems, in short). The ivFRSN P systems can better characterize uncertain alarm information in power systems. Firstly, the modeling approach and fuzzy reasoning algorithm are developed. Secondly, the corresponding fault diagnosis models are discussed. Finally, the fault diagnosis of a six-bus 69kV distribution system is used as an example, including single fault with device failure and multiple faults, to demonstrate the availability and effectiveness of the proposed fault diagnosis model based on ivFRSN P systems.
Haina RONG, Ming ZHU, Zhipeng FENG, Gexiang ZHANG, Kang HUANG
Abstract. A novel approach for classifying different types of faults occurring in power transmission lines is proposed by considering wavelet transform, singular value decomposition and Fuzzy Reasoning Spiking Neural P Systems (FRSNPS). In this approach, singular value decomposition in wavelet domain is used to extract features of fault current components recorded from power transmission lines; FRSNPS is applied to build the fault type classification model. Several cases with different fault types in power transmission lines are considered in the simulation experiments to verify the effectiveness of the proposed approach. The robustness to noise and to parameters of power transmission lines is also discussed. Read the pdf
Xun WANG, Tao SONG, Pan ZHENG, Shaohua HAO, Tongmao MA
Abstract. Spiking neural P systems with anti-spikes (shortly named ASN P systems) are a class of distributed and parallel neural-like computing systems. Besides spikes, neurons in ASN P systems can also contain a number of anti-spikes. Whenever spikes and anti-spikes meet in a neuron, they annihilate each other immediately in a maximal manner, that is, the annihilation has priority over neuron's spiking. In this work, we introduce a variant of ASN P systems, named ASN P systems without annihilating priority. In such systems, when a neuron has both a number of spikes and anti-spikes, the annihilation between spikes and anti-spikes is not obligatory and the neuron can choose non-deterministically spiking or annihilating. The computational power of ASN P systems without annihilating priority as number generators is investigated. As a result, it is obtained that such system with at most two rules per neuron can achieve Turing completeness as number generators. This result gives an answer to an open problem formulated in [INT J COMPUT COMMUN & CONTROL, 3, 273 - 282, 2009]. As well, the obtained result is optimal in the sense of having a minimal number of rules in neurons of Turing universal ASN P systems. Read the pdf
Jym Paul A. CARANDANG, John Matthew B. VILLAFLORES, Francis George C. CABARLE, Henry N. ADORNA, Miguel A. MARTINEZ-DEL- AMOR
Abstract. Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. CuSNP is a project involving sequential (CPU) and parallel (GPU) simulators for SN P systems. In this work, we report the following results: a P-Lingua file parser is included, for ease of use when performing simulations; extension of the matrix representation of SN P systems to include delay; comparison and analysis of our simulators by simulating two types (bitonic and generalized) of parallel sorting networks; extension of supported types of regular expressions in SN P systems. Our GPU simulator is better suited for generalized sorting as compared to bitonic sorting networks, and the GPU simulators run up to 50x faster than our CPU simulator. Finally, we discuss our experiments and provide directions for further work. Read the pdf
Luis VALENCIA-CABRERA, Tingfang WU, Zhiqiang ZHANG, Linqiang
Abstract. Spiking neural P systems (SN P systems, for short) constitute a class of computing models in the research field of membrane computing. Inspired by the interactions among neurons in the brain, they have attracted much attention since their appearance in 2006. Many variants have emerged, presenting a graph-based structure, and several software simulators were developed for them. Recently, a different approach was proposed by introducing cell-like spiking neural P systems. Unlike previous SN P systems, this new model includes a tree-based structure, taking elements from traditional rewriting rules in the original P systems. In this work, a software tool within the framework of P-Lingua and MeCoSim is presented. This software may play an important role assisting in tasks of design, simulation and experimental validation. Read the pdf
Andrei ZENE, Claudiu-Teodor CHIRAP, Octavian CREŢ, Lucia VĂCARIU
Abstract. The NoC domain has known a big development lately, acquiring an ever growing importance in the context of hardware miniaturization. However at this point it is still hard to observe and / or debug what is going on inside the chip, be it for debugging purposes or for long-running processes like computational biology simulations which could gain a great improvement when ran on an FPGA chip. Therefore, this paper proposes hardware implementations of three of the most important snapshot algorithms: Lai-Yang, Li et al. and Mattern, which could be used in order to achieve observability on a long-running process physically implemented on an FPGA target. The setup is based on the layers architecture, making it easy to separate the snapshot algorithm from the application or the intercommunication network. The intercommunication network was generated using the CONNECT NoC generator and the snapshots are sent to a PC via UART for displaying. The algorithms were compared from three points of view: operating frequency, throughput and resource usage. Based on the obtained results, we show that the Mattern algorithm is the best candidate for an effective hardware implementation (both from the resource usage and speed points of view). Read the pdf
Cornel MICLEA, Luminiţa AMARANDE, Marius Cristian CIOANGHER,
Abstract. Water splitting by means of semiconducting photoelectrodes and solar light represents a promising alternative to conventional fossil fuel economy. In this process the photoactive electrode absorb sunlight directly thus initiating the photochemical reaction which create excess electrons in the conduction band of the semiconducting electrode. Titanium doped iron oxide seems to be a promising semiconducting material for photoelectrodes. Consequently, we investigated the effect of Ti doping on the structure, electrical and photoelectrochemical properties α-Fe2O3. The Ti doped α-Fe2O3 were prepared by a slightly modified mixed oxide route, consisting in a prolonged mixing of the raw materials in a high energy planetary ball mill until the particles decreased to the nanometric sizes. Optimum results were obtained for samples doped with 5 at. % titanium and sintered at 1200°C.