Experimental Study on the Effect of Additives on Drainage Consolidation in Vacuum Preloading Combined with Electroosmosis

Jianli Hu,Xiaobing Li,Dikang Zhang,Jun Wang,Xiuqing Hu,Yuanqiang Cai 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.9

Vacuum-electroosmosis is a common method of foundation treatment; however, it is disadvantaged by fine soil particles clogging the drainage plate. To overcome this issue, this study treated the dredged fill from vacuum-electroosmosis with different additives. The parameters of discharged water, current, water content, and shear strength of soils treated with different amounts of Ca(OH)2, FeCl3, and NaCl were analyzed. The results showed that different additives have different effects on the vacuum-electroosmosis method for reinforcing dredger filling. Excessive additive contents were found to have an adverse effect on vacuum-electroosmosis, and hence, optimum amounts are required for the three additives. On comparing the optimum dosage of these additives, FeCl3 was observed to be the most energy-saving. From the aspect of anode corrosion and the costs involved, Ca(OH)2 was the most economical. For practical engineering applications, Ca(OH)2 was the most preferable additive. The results of this study provide guidance and scientific criteria for similar dredging foundation treatments.

Maximizing the Lifetime of Embedded Systems Powered by Fuel Cell-Battery Hybrids

Jianli Zhuo,Chakrabarti, C.,Kyungsoo Lee,Naehyuck Chang,Vrudhula, S. Institute of Electrical and Electronics Engineers 2009 IEEE transactions on very large scale integration Vol.17 No.1

<P>Fuel cell (FC) is a viable alternative power source for portable applications; it has higher energy density than traditional Li-ion battery and thus can achieve longer lifetime for the same weight or volume. However, because of its limited power density, it can hardly track fast fluctuations in the load current of digital systems. A hybrid power source, which consists of a FC and a Li-ion battery, has the advantages of long lifetime and good load following capabilities. In this paper, we consider the problem of extending the lifetime of a fuel-cell-based hybrid source that is used to provide power to an embedded system which supports dynamic voltage scaling (DVS). We propose an energy-based optimization framework that considers the characteristics of both the energy consumer (the embedded system) and the energy provider (the hybrid power source). We use this framework to develop algorithms that determine the output power level of the FC and the scaling factor of the DVS processor during task scheduling. Simulations on task traces based on a real-application (Path Finder) and a randomized version demonstrate significant superiority of our algorithms with respect to a conventional DVS algorithm which only considers energy minimization of the embedded system.</P>

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

( Jianli Zhao ),( Qinzhi Zhang ),( Qiuxia Sun ),( Huan Huo ),( Yu Xiao ),( Maoguo Gong ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.1

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

The Scheme of Power Allocation for Decode-and-Forward Relay Channel in Energy Harvesting WSNs

Jianli Xie,Cuiran Li 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.12

Recently, the issue of excessive energy consumption in wireless communications has become increasing critical, and the energy harvesting as a renewable energy resource, has received extensive attractions. In this paper, a wireless sensor network (WSN) is considered, where the source-destination pair communicates via an energy harvesting relay links. We study the problem of the harvested energy distribution among the source, relay and destination nodes. An effective power allocation scheme is developed which exploits the decode-and-forward (DF) relaying strategy and channel state information. The outage probability is analyzed and simulation results show that the outage performance for two sub-channels is always performs well in the cases of different threshold target data rate. Moreover, the effect of the different radio of the optimal sub-channel gain and Rayleigh channel gain on the outage performance is evaluated.

ULTRAFINE AU NANODOTS ON GRAPHENE OXIDE FOR CATALYTIC REDUCTION OF 4-NITROPHENOL

JIANLI CHEN,GANG CHENG,ZHUANGNAN LI,FUJUN MIAO,XIAOQIANG CUI,WEITAO ZHENG 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2013 NANO Vol.8 No.3

Graphene oxide nanosheet is an ideal platform to capture nanoparticles for highly efficient catalysis, electrochemical sensing and biosensing. In this work, we have described a simple synthesis method for preparation graphene oxide–Au nanohybrid. Au nanodots with an average size of 1.6 nm uniformly dispersed on the surface of graphene oxide. The well-defined nanostructure has been characterized by transmission electron microscopy (TEM) and atomic force microscopy (AFM). The nanohybrid also exhibits enhanced catalytic activity toward the reduction of 4-nitrophenol by NaBH4. Comparing with pure Au nanodots and graphene oxide, graphene oxide–Au nanohybrid shows the highest catalytic activity. This approach not only suggests a wide potential application of graphene oxide nanosheet as a host material for supporting a variety of nanoparticles, but also provides a new approach for the fabrication of graphene-based nanohybrids with multiple physical and chemical properties.

MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

( Jianli Zhao ),( Zhengbin Fu ),( Qiuxia Sun ),( Sheng Fang ),( Wenmin Wu ),( Yang Zhang ),( Wei Wang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.5

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

Carbon dispersed iron-manganese catalyst for light olefin synthesis from CO hydrogenation

Jianli Zhang,Kegong Fang,Kan Zhang,Wenhuai Li,Yuhan Sun 한국화학공학회 2009 Korean Journal of Chemical Engineering Vol.26 No.3

High performance iron-manganese catalysts dispersed with carbon to produce light olefins from CO hydrogenation were prepared by sol-gel method using citric acid as precursor. The effects of carbon content on the bulk structure, the water gas shift reaction, the chain propagation ability and the activity and selectivity of the catalysts were investigated. The results showed that the catalysts were gradually reduced during the decomposition of the precursor when calcined under pure N2. The formation of iron-manganese mixed crystallites was favored and stabilized because of the enhanced interaction of iron and manganese with increasing carbon content. During the subsequent CO hydrogenation reaction, all the catalysts showed high activity and olefin selectivity. With increasing carbon content, the water gas shift (WGS) reaction was restrained and the chain propagation ability was inhibited. Catalysts with higher carbon content showed much lighter hydrocarbon products; however, the selectivity of CH4 was almost unchanged.

User Bias Drift Social Recommendation Algorithm based on Metric Learning

Jianli Zhao,Tingting Li,Shangcheng Yang,Hao Li,Baobao Chai 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user’s potential preferences, reduces algorithms’ recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user’s preferences, ignoring the direct impact on user’s rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user’s ratings preferences and user’s preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Jianli Xie,Cuiran Li,Hui Zhou 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.3

Weight based clustering has become the mainstream clustering algorithm in low-speed Ad hoc networks because of its excellent cluster stability. However, due to the dynamic topology changing in high-speed Ad hoc network, the cluster stability (network stability) decreased and the cluster maintenance costs increased sharply. To solve the problem, we propose a dynamic entropy based combination weighted clustering approach (DECW). First, according to the history messages of an evaluation node in the network, the upper bound and the lower bound value of each clustering index will be recorded, so the information entropy deviation of the indexes and dynamic entropy weight of each node can be obtained. After, the linear combination weights set of evaluation nodes is modeled as the second-order norm game , and the weight vector deviation is minimized as the optimization goal to get the multi-node dynamic entropy weights. In the cluster maintenance, a new Monte Carlo optimization is proposed to avoid the frequent cluster-heads (CHs) replacement induced of high node mobility of. Simulation results reveal that the proposed approach has the better adaptability in high-speed mobile environment.

3-frame 위상 랩핑 방법을 이용한 3차원 형상 재구현에 관한 연구

Jianli Shang,곽민우,이창원,황보승 대한전기학회 2012 대한전기학회 학술대회 논문집 Vol.2012 No.7