Comprehensive evaluation research of hybrid energy systems driven by renewable energy based on fuzzy multi-criteria decision-making

The worsening of climate conditions is closely related to the large amount of carbon dioxide produced by human use of fossil fuels. Under the guidance of the goal of “carbon peaking and carbon neutrality goals”, with the deepening of the structural reform of the energy supply side, the hybrid energy system coupled with renewable energy has become an important means to solve the energy problem. This paper focuses on the comprehensive evaluation of hybrid energy systems. A complete decision support system is constructed in this study. The system primarily consists of four components: 1) Twelve evaluation criteria from economic, environmental, technological, and socio-political perspectives; 2) A decision information collecting and processing method in uncertain environment combining triangular fuzzy numbers and hesitation fuzzy language term sets; 3) A comprehensive weighting method based on Lagrange optimization theory; 4) Solution ranking based on the fuzzy VIKOR method that considers the risk preferences of decision-makers. Through a case study, it was found that the four most important criteria are investment cost, comprehensive energy efficiency, dynamic payback period and energy supply reliability with weights of 7.21%, 7.17%, 7.17%, and 7.15% respectively. A1 is the scheme with the best comprehensive benefit. The selection of solutions may vary depending on the decision-maker’s risk preference. Through the aforementioned research, the decision framework enables the evaluation of the overall performance of the system and provides decision-making references for decision-makers in selecting solutions

Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

Subspace method for blind equalization of multiple time-varying FIR channels

Wireless communications is the fastest growing segment of communication technologies. In a wireless communication system, the inter-symbol interference (ISI) is a linear distortion which causes decision errors at the receiver. The equalizer is required to remove the ISI. In the past decade, the blind channel equalization has been a popular research topic in the area of wireless communication. A particular class of blind equalization approaches is based on the second order statistics (SOS) of the received signals. Within this framework, subspace methods exploit the orthogonality between the signal and noise subspaces in order to identify the channel characteristics so that the equalizer can be constructed.This thesis investigates a new equalization algorithm for the time-varying (TV) channel under the single-input multiple-output (SIMO) framework. The TV channel is decomposed using arbitrary basis functions associated with time variable properties of the channels, and with expansion coefficients associated with multi-path delays. An equivalent time-invariant (TI) multiple-input multiple-output (MIMO) system is built for the TV SIMO system. The equivalent TI MIMO system is assumed to match the necessary and sufficient conditions of the SOS identification framework. The blind subspace method is exploited to identify the expansion coefficients when considered as channel characteristics of the MIMO system. The associated ambiguity matrix is identified by using the least square (LS) method. The zero forcing equalizer is realized based on the result of the subspace channel equalization and the ambiguity matrix. The simulation results indicate that the proposed equalizer can effectively recover the source signal in TV SIMO channel applications.La communication sans fil est le segment de croissance le plus dynamique parmi les techniques de la communication. Dans un système de communication sans fil, l'interférence inter-symboles (ISI) est une distorsion linéaire qui provoque des erreurs de décisions au niveau du récepteur. L'égaliseur est nécessaire pour éliminer l'ISI. Récemment, l'égalisation aveugle du canal est devenue un sujet de recherche populaire dans les domaines de la communication sans fil. Un des jalons de la technologie aveugle est fondé sur le cadre des statistiques du second ordre (SOS) du signal reçu. Tout particulièrement, la méthode du sous-espace exploite l'orthogonalité entre le sous-espace signal et le sous-espace bruit afin d'identifier les caractéristiques du canal de telle sorte que l'égaliseur puisse être construit. Dans cette thèse, j'ai proposé un algorithme de péréquation pour le canal à variation temporelle (TV) des systèmes à entrée unique et sorties multiples (SIMO). Le canal TV est décomposé en fonctions arbitraires associées aux propriétés de TV du cacal, et avec les coefficients d'expansion associés à chacun des retards multi-trajet. Un système équivalent invariant dans le temps (TI), à entrée multiples et sorties multiples (MIMO) est conçu pour le TV SIMO. Le systèmeéquivalent TI MIMO est supposé correspondre aux conditions nécessaires et suffisantes dans le cadre de la théorie SOS. La méthode sous-espace aveugle est exploitée pour identifier les coefficients d'expansion quand ils sont considérés comme caractéristiques du canal du système MIMO. La matrice d'ambiguïté est déterminée par la méthode des moindres carrés (LS). La remise à zéro forcée de l'égaliseur est réalisée sur la base des résultats de l'égalisation des canaux de sous-espace et de la matrice d'ambiguïté. Des expériences de simulations numériques sont utilisées afin de démontrer le potential d'application de la nouvelle méthode

Comparison on mechanical properties of heavily phosphorus- and arsenic-doped Czochralski silicon wafers

Heavily phosphorus (P)- and arsenic (As)-doped Czochralski silicon (CZ-Si) wafers generally act as the substrates for the epitaxial silicon wafers used to fabricate power and communication devices. The mechanical properties of such two kinds of n-type heavily doped CZ silicon wafers are vital to ensure the quality of epitaxial silicon wafers and the manufacturing yields of devices. In this work, the mechanical properties including the hardness, Young’s modulus, indentation fracture toughness and the resistance to dislocation motion have been comparatively investigated for heavily P- and As-doped CZ-Si wafers. It is found that heavily P-doped CZ-Si possesses somewhat higher hardness, lower Young’s modulus, larger indentation fracture toughness and stronger resistance to dislocation motion than heavily As-doped CZ-Si. The mechanisms underlying this finding have been tentatively elucidated by considering the differences in the doping effects of P and As in silicon

Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. In addition, an aligned time coding and an auxiliary transition scheme are carefully devised for batched training on unaligned sequences. Our model can be trained effectively using stochastic variational inference and generates probabilistic predictions with Monte-Carlo simulation. Furthermore, our model produces accurate, sharp and more realistic probabilistic forecasts. We also show that modeling asynchronous event sequences is crucial for multi-horizon time-series forecasting

A Sensitivity-Based Improving Learning Algorithm for Madaline Rule II

This paper proposes a new adaptive learning algorithm for Madalines based on a sensitivity measure that is established to investigate the effect of a Madaline weight adaptation on its output. The algorithm, following the basic idea of minimal disturbance as the MRII did, introduces an adaptation selection rule by means of the sensitivity measure to more accurately locate the weights in real need of adaptation. Experimental results on some benchmark data demonstrate that the proposed algorithm has much better learning performance than the MRII and the BP algorithms

Shared metabolic shifts in endothelial cells in stroke and Alzheimer’s disease revealed by integrated analysis

Abstract Since metabolic dysregulation is a hallmark of both stroke and Alzheimer’s disease (AD), mining shared metabolic patterns in these diseases will help to identify their possible pathogenic mechanisms and potential intervention targets. However, a systematic integration analysis of the metabolic networks of the these diseases is still lacking. In this study, we integrated single-cell RNA sequencing datasets of ischemic stroke (IS), hemorrhagic stroke (HS) and AD models to construct metabolic flux profiles at the single-cell level. We discovered that the three disorders cause shared metabolic shifts in endothelial cells. These altered metabolic modules were mainly enriched in the transporter-related pathways and were predicted to potentially lead to a decrease in metabolites such as pyruvate and fumarate. We further found that Lef1, Elk3 and Fosl1 may be upstream transcriptional regulators causing metabolic shifts and may be possible targets for interventions that halt the course of neurodegeneration