PET熔体输送系统中熔体温度自动控制方案

在热媒温度控制系统中以熔体温度作为主参数,组成串极调节系统。投入运行后,熔体温度稳定,调节阀动作平稳,效果良好。     

相位测量定位法在焦炉车辆上的应用与研究

文中通过测量载波相位进行定位的方法来解决焦炉车辆的定位问题，以达到无线定位的目的。并对定位的数学模型进行了详细的分析与论述。     

新桥硫铁矿二步骤回采工艺及充填方案的探讨

介绍了新桥硫铁矿二步骤回采工艺，并从技术经济角度对该矿所采用的充填料及充填系统进行了探讨。     

A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post

Postal logistics has a complex transportation network for efficient mail delivery. Therefore, a postal logistics network consists of various functional sites with a hybrid hub-and-spoke structure. More specifically, there are multiple Delivery & Pickup Stations (D&PSs), multiple Mail Processing Centers (MPCs), and one Exchange Center (EC). In this paper, we develop two mathematical models with realistic restrictions for Korea Post for the current postal logistics network by simultaneously considering locations and allocations. We propose an Integer Linear Programming (ILP) model for transportation network organization and vehicle operation and a Mixed Integer Linear Programming (MILP) model that considers potential ECs for decision making while simultaneously regarding the EC location, transportation network organization, and vehicle operation. We use modified real data from Korea Post. Additionally, we consider several scenarios for supporting EC decision makers. The proposed models and scenarios are very useful in decision making for postal logistics network designers and operators.     

结合矩阵分解的混合型社会化推荐算法

推荐系统是用来解决当今时代信息过载的重要工具。随着在线社交网络的出现和普及,一些基于网络推荐算法研究的出现,已经引起研究者的广泛关注。信任是社会网络中的重要信息之一,通常用来改进基于社交网络的推荐系统,然而,大多数信任感知的推荐系统忽略了用户有不同行为偏好在不同的兴趣域;本文不仅考虑了用户间特定域信任网络,并且结合推荐项目之间特征属性信息,提出了一种新型社会化推荐算法(H-PMF)。实验表明,H-PMF算法在评分误差和推荐精度上都取得了更好的效果。     

氯乙烯悬浮聚合分散体系的中试研究

通过大量试验分析了HPMC-E50和HPMC-F50在氯乙烯悬浮聚合中对PVC树脂的影响,从而确定了复合分散体系的质量分数以及分散剂的配比。结果表明,PVA和HPMC-E50复合分散体系的分散能力优于PVA和HPMC-F50复合分散体系;在悬浮聚合中,PVA和HPMC-E50复合分散体系与VCM的比例以0.05%~0.06%为宜;在PVA和HPMC-E50复合分散体系中,HPMC-E50所占比例以40%~60%为宜。     

前大油田注水系统防腐防垢技术的研究

前大油田注水系统腐蚀、结垢问题十分突出,开发了适用于前大油田注水水质的JYSJ-Q型杀菌剂、JYHZJ-Q型缓蚀阻垢剂,并通过试验确定了配套加药工艺.经现场应用,有效缓解了注水系统的腐蚀结垢问题,获得了显著的社会效益及经济效益.     

基于Flash的关键变量容错存储技术研究

嵌入式系统中往往需要保存少量复位或掉电后依赖的关键变量,利用处理器片内Flash存储参数已经成为常规选择,但与常规Flash存储器件相比,片内Flash擦除次数更为有限,难以满足系统生命周期要求。研究了如何延长微处理器片内Flash使用寿命以及关键系统变量容错存储技术,针对典型嵌入式系统应用,分析Flash擦写管理机制,基于写磨损均衡算法进行改良,进一步提升系统容错能力。该技术在基于STM32单片机硬件平台进行了测试验证。     

Reliability-based robust design optimization: A general methodology using genetic algorithm

In this paper, we present an improved general methodology including four stages to design robust and reliable products under uncertainties. First, as the formulation stage, we consider reliability and robustness simultaneously to propose the new formulation of reliability-based robust design optimization (RBRDO) problems. In order to generate reliable and robust Pareto-optimal solutions, the combination of genetic algorithm with reliability assessment loop based on the performance measure approach is applied as the second stage. Next, we develop two criteria to select a solution from obtained Pareto-optimal set to achieve the best possible implementation. Finally, the result verification is performed with Monte Carlo Simulations and also the quality improvement during manufacturing process is considered by identifying and controlling the critical variables. The effectiveness and applicability of this new proposed methodology is demonstrated through a case study.     

TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations.In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.