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| 1 | Principles and construction of MSD adder in ternary optical computer显示文摘The two remarkable features of ternary values and a massive unit with thousands bits of parallel computation will make the ternary optical computer (TOC) with modified signed-digit (MSD) adder more powerful and e?cient than ever before for numerical calculations. Based on the decrease-radix design presented previously, a TOC can satisfy either a user requiring huge capacity for data calculations or one with a moderate amount of data, if it is equipped with a prepared adder. Furthermore, with the application of pipelined operations and the proposed data editing technique, the e?ciency of the prepared adder can be greatly improved, so that each calculated result can be obtained almost within one clock cycle. It is hopeful that by employing a MSD adder, users will be able to enter a new dimension with the creation of a new multiplier, new divider, as well as new matrix operator in a TOC in the near future. | JIN Yi SHEN YunFu PENG JunJie XU ShiYi DING GuangTai YUE DongJian YOU HaiHang | 2010 | Science China(Information Sciences)2010，53，11： | 26 |
| 2 | Empirical investigation of stochastic local search for maximum satisfiability显示文摘The maximum satisfiability (MAX-SAT)problem is an important NP-hard problem in theory,and has a broad range of applications in practice.Stochastic local search (SLS)is becoming an increasingly popular method for solving MAX-SAT.Recently,a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT instances.However,the performance of CCLS on solving industrial MAX-SAT instances lags far behind.In this paper,we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAXSAT instances.First,we conduct experiments to analyze why CCLS performs poor on industrial instances.Then we propose a new strategy called additive BMS (Best from Multiple Selections)to ease the serious issue.By integrating CCLS and additive BMS,we develop a new SLS algorithm for MAXSAT called CCABMS,and related experiments indicate the efficiency of CCABMS.Also,we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT,and combine an effective initialization method with CCABMS,resulting in an enhanced algorithm.Experimental results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances. | Yi CHU Chuan LUO Shaowei CAI Haihang YOU | 2019 | Frontiers of Computer Science2019，13，1： | 3 |
| 3 | A numerical study on smoke behaviors in inclined tunnel fires under natural ventilation显示文摘To investigate the effect of tunnel slope on hot gas movement and smoke distribution in a slopping tunnel fire,a series of tunnel fire models are built by fire dynamics simulator(FDS),with a slope varies from 0 to 10%.Parameters such as ceiling temperature and airflow velocity are measured.The results indicate that the relationship between smoke back-layering length and tunnel slope can be described as an exponential function.The smoke temperature at the downstream exit first increased and then decreased with a higher slope.The airflow velocity at downstream outlet increased nonlinearity when tunnel slope was less than 8%.In the slope tunnel,the fire smoke spread process can be divided into three stages.Fire smoke spreads upstream to the peak distance,subsequently,the upstream smoke layer decreases gradually,the tunnel fire reaches a quasi-steady state.The backflow characteristics of smoke in sloped tunnels are coupled with the downstream length and outlet smoke temperature.In the initial stage of a slope tunnel fire,smoke spreads upstream for a long distance,endangering human health. | Jie Kong Wenjiao You Zhisheng Xu Hui Liu Haihang Li | 2022 | Journal of Safety Science and Resilience2022，3，2： | 1 |
| 4 | Coupling climate and hydrological models: Interoperability through Web Services显示文摘 | Jonathan L. Goodall Kathleen D. Saint Mehmet B. Ercan Laura J. Briley Sylvia Murphy Haihang You Cecelia DeLuca Richard B. Rood | 2013 | Environmental Modelling and Software2013，，： | 1 |
| 5 | Fast and efficient parallel breadth-first search with power-law graph transformation显示文摘1 Introduction Most real-world graphs are large-scale but unstructured and sparse.One of the most notable characteristics of real-world graphs is the skewed power law degree distribution[1]:most vertices have a few neighbors while a few own a large number of neighbors.These characteristics present challenges for efficient parallel graph processing,such as load imbalance,poor locality,and redundant computations.Apart from modifying the graph programming abstraction or changing the execution models on different architectures,reducing the irregularity of graph data also improves the performance of graph processing[2].For example,it is wellknown that BFS has a bad temporal locality,but it is possible to transform irregular graphs to more regular ones to improve spatial locality and gain more performance. | Zite JIANG Tao LIU Shuai ZHANG Mengting YUAN Haihang YOU | 2022 | Frontiers of Computer Science2022，16，5： | 0 |
| 6 | A hybrid-order local search algorithm for set k-cover problem in wireless sensor networks显示文摘1 Introduction The lifetime of wireless sensor networks(WSNs)is restricted by the limited energy of battery-powered sensor devices,making the lifetime extension a critical problem in real applications[1].Several aspects have been examined in previous works to extend the lifetime of WSNs,such as the deployment position,the network routing strategy and the sensing range adjustment.Given the fact that there are often many redundant sensors,a practical way to extend the lifetime of a WSN is to partition the sensors into subsets,each of which can cover all the targets[2].Then the sets are activated one by one,extending the lifetime of a WSN to times of the battery lifetime of a sensor.The problem of finding the maximal is abstracted as the set k-cover problem. | Boxiao LIU Mengting YUAN Haihang YOU | 2023 | Frontiers of Computer Science2023，17，3： | 0 |
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