The fifth generation (5G) mobile communication systems will be in use around 2020. The aim of 5G systems is to provide anywhere and anytime connectivity for anyone and anything. Several new ...technologies are being researched for 5G systems, such as massive multiple-input multiple-output communications, vehicle-to-vehicle communications, high-speed train communications, and millimeter wave communications. Each of these technologies introduces new propagation properties and sets specific requirements on 5G channel modeling. Considering the fact that channel models are indispensable for system design and performance evaluation, accurate and efficient channel models covering various 5G technologies and scenarios are urgently needed. This paper first summarizes the requirements of the 5G channel modeling, and then provides an extensive review of the recent channel measurements and models. Finally, future research directions for channel measurements and modeling are provided.
The key aspect in coal production is realizing safe and efficient mining to maximize the utilization of the resources. A requirement for sustainable economic development is realizing green coal ...production, which is influenced by factors of coal economic, energy, ecological, coal gangue economic and social benefits. To balance these factors, this paper proposes a many-objective optimization model with five objectives for green coal production. Furthermore, a hybrid many-objective particle swarm optimization (HMaPSO) algorithm is designed to solve the established model. A new offspring of the alternative pool is generated by employing different evolutionary operators. The environmental selection mechanism is adopted to select and store the excellent solutions. Two sets of experiments are performed to verify the effectiveness of the proposed approach: First, the HMaPSO algorithm is tested on the DTLZ functions, and its performance is compared with that of several widely used many-objective algorithms. Second, the HMaPSO algorithm is applied to solve the many-objective green coal production optimization model. The computational results demonstrate the effectiveness of the proposed approach, and the simulation results prove that the designed approach can provide promising choices for decision makers in regional planning.
Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods ...resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely, the weighted Schatten p-norm minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex lp-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model the complex and dynamic scenes compared with state-of-the-art methods.
With accelerated ensemble of the Internet of Things technology and automotive industry, vehicular network has been established as powerful tools. However, it is a significant challenge for dynamic ...and heterogeneous vehicular network to meet high requirements of the sixth-generation (6G) network such as high reliability and high security. To address this challenge, we design a novel weight-based ensemble machine learning algorithm (WBELA) to identify abnormal messages of vehicular Controller Area Network (CAN) bus network. Then, we establish a model based on many-objective optimization for intrusion detection of CAN bus network. To support this model, a many-objective optimization algorithm based on balance convergence and diversity (MaOEA-BCD) is designed. Open-source CAN bus message data sets and tamper attack scenarios are used to evaluate the effectiveness of proposed algorithm for different ID data frames. Experimental results revealed that proposed methods significantly enhance precision, reduce the false positive rate and have better performance than other methods so as to enhance security of vehicular networks in 6G.
The Twist Tensor Nuclear Norm for Video Completion Hu, Wenrui; Tao, Dacheng; Zhang, Wensheng ...
IEEE transaction on neural networks and learning systems,
12/2017, Volume:
28, Issue:
12
Journal Article
In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor representation to ...laterally store 2-D data slices in order. On one hand, t-TNN convexly relaxes the tensor multirank of the twist tensor in the Fourier domain, which allows an efficient computation using fast Fourier transform. On the other, t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain, which extends the traditional matrix nuclear norm in a block circulant way. We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness, especially when dealing with video recorded by a nonstationary panning camera. The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance. This representation, after transformation, exploits the horizontal translation relationship between the frames in a video, and endows the t-TNN model with a more powerful ability to reconstruct panning videos than the existing state-of-the-art low-rank models.
Abstract
For prostate cancer (PCa) patients, biochemical recurrence (BCR) is the first sign of disease relapse and the subsequent metastasis.
TP53
mutations are relatively prevalent in advanced PCa ...forms. We aimed to utilize this knowledge to identify robust transcriptomic signatures for BCR prediction in patients with Gleason score ≥ 7 cancers, which cause most PCa deaths. Using the TCGA-PRAD dataset and the novel data-driven stochastic approach proposed in this study, we identified a 25-gene signature from the genes whose expression in tumors was associated with TP53 mutation statuses. The predictive strength of the signature was assessed by AUC and Fisher’s exact test p-value according to the output of support vector machine-based cross validation. For the TCGA-PRAD dataset, the AUC and p-value were 0.837 and 5 × 10
–13
, respectively. For five external datasets, the AUCs and p-values ranged from 0.632 to 0.794 and 6 × 10
–2
to 5 × 10
–5
, respectively. The signature also performed well in predicting relapse-free survival (RFS). The signature-based transcriptomic risk scores (TRS) explained 28.2% of variation in RFS on average. The combination of TRS and clinicopathologic prognostic factors explained 23–72% of variation in RFS, with a median of 54.5%. Our method and findings are useful for developing new prognostic tools in PCa and other cancers.
Various titanium metallurgical processes have been reviewed and compared for titanium dioxide and titanium metal, mainly focusing on the future development of hydrometallurgical processes. It is ...recognised that ilmenite is becoming increasingly important due to the rapid depletion of natural rutile. Many processes are commercially used or proposed to upgrade ilmenite to synthetic rutile. Most of these processes involve a combination of pyrometallurgy and hydrometallurgy and are generally expensive.
The commercialised thermo-chemical chloride processes such as Kroll and Hunter processes are batch operations and need higher grade natural rutile or upgraded synthetic rutile and slag as the feed and the involvement of cost sensitive chlorination and thermo steps. Many improvements for the thermo-chemical processes have been made, but they hold little potential for significant cost reductions beyond current technology. The development of the electro-chemical processes for direct reduction of TiO
2 and electro-slag as feed material and
in-situ electrolysis has achieved some success. However, some challenging issues such as redox cycling, feeding, kinetics, control heat balance have to be resolved before scaling-up to commercial applications.
Direct hydrometallurgical leach processes are advantageous in processing abundant ilmenite ores, low energy consumption and produce sufficiently high quality of pigment grade TiO
2 products for a wide range of applications and major demand. Novel BHP Billiton sulphate processes have been developed to improve leaching strategies, separation of metals by solvent extraction, reduced wastes and recycling acids, and very promising for commercial applications in future. Direct chloride leaching processes have been investigated intensively, featuring purification by solvent extraction and reclaiming HCl by hydrolysis or pyrohydrolysis. Caustic leach with high selectivity and titanium dioxide nano-technology has also been developed. Further development of direct leaching ilmenite coupled with solvent extraction for titanium pigment and metal production, is recommended.
► Various titanium metallurgical processes for the production of titanium dioxide and titanium metal have been reviewed and compared including: ► Processes to upgrade ilmenite to synthetic rutile. ► Thermo-chemical Kroll and Hunter processes. ► Electro-chemical processes for direct reduction of TiO
2 and electro-slag and
in-situ electrolysis. ► Direct hydrometallurgical leach processes.
The world rapidly growing demand for manganese has made it increasingly important to develop processes for economical recovery of manganese from low grade manganese ores and other secondary sources. ...Part I of this review outlines metallurgical processes for manganese production from various resources, particularly focusing on recent developments in direct hydrometallurgical leaching and recovery processes to identify potential sources of manganese and products which can be economically produced.
High grade manganese ores (>40%) are typically processed into suitable metallic alloy forms by pyrometallurgical processes. Low grade manganese ores (<40%) are conventionally processed by pyrometallurgical reductive roasting or melting followed by hydrometallurgical processing for production of chemical manganese dioxide (CMD), electrolytic manganese (EM) or electrolytic manganese dioxide (EMD).
Various direct reductive leaching processes have been studied and developed for processing low manganese ores and ocean manganese nodules, including leaching with ferrous iron, sulfur dioxide, cuprous copper, hydrogen peroxide, nitrous acid, organic reductants, and bio- and electro-reductions. Among these processes, the leaching with cheap sulfur dioxide or ferrous ion is most promising and has been operated in a pilot scale. The crucial issue is the purification of leach liquors and the selective recovery of copper, nickel and cobalt is often difficult from solutions containing soluble iron and manganese. For treatment of manganese bearing materials including waste batteries, spent electrodes, sludges, slags and spent catalysts, a leaching or reductive leaching step is generally needed followed by various purification steps, which makes the processes less economically viable.
It is concluded that the recovery of manganese from nickel laterite process effluents which contain 1–5 g/L Mn offers a growing low cost resource of manganese. Part II of this review considers the application of various solvent extraction reagents and precipitation methods for treating such manganese liquors.
Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. Although many researchers have attempted to explain the origins of AD, developing an effective strategy in AD clinical ...therapy is difficult. Recent studies have revealed a potential link between AD and circRNA-associated-ceRNA networks. However, few genome-wide studies have identified the potential circRNA-associated-ceRNA pairs involved in AD. In this study, we systematically explored the circRNA-associated-ceRNA mechanism in a 7-month-old senescence-accelerated mouse prone 8 (SAMP8) model brain through deep RNA sequencing. We obtained 235 significantly dysregulated circRNA transcripts, 30 significantly dysregulated miRNAs, and 1,202 significantly dysregulated mRNAs. We then constructed the most comprehensive circRNA-associated-ceRNA networks in SAMP8 brain. GO analysis revealed that these networks were involved in regulating the development of AD from various angles, for instance, axon terminus (GO: 0043679) and synapse (GO: 0045202). Following rigorous selection, we discovered that the circRNA-associated-ceRNA networks in this AD mouse model were mainly involved in the regulation of Aβ clearance (Hmgb2) and myelin function (Dio2). This research is the first to provide a systematic dissection of circRNA-associated-ceRNA profiling in SAMP8 mouse brain. The selected circRNA-associated-ceRNA networks can profoundly affect the diagnosis and therapy of AD in the future.
circRNA inhibits the function of miRNA as miRNA sponges through the ceRNA network. Zhang et al. explored mouse brain genome-wide circRNA-associated-ceRNA networks between 7-month-old SAMP8 and SAMR1 models through deep RNA-seq and listed two ceRNA (circRNA-associated-ceRNA)-related genes (Hmgb2 and Dio2) that were most likely involved in AD.
Most millimeter wave (mmWave) channel measurements are conducted with different configurations, which may have large impacts on propagation channel characteristics. In addition, the comparison of ...different mmWave bands is scarce. Moreover, mmWave massive multiple-input multiple-output (MIMO) channel measurements are absent, and new propagation properties caused by large antenna arrays have rarely been studied yet. In this paper, we carry out mmWave massive MIMO channel measurements at 11-, 16-, 28-, and 38-GHz bands in indoor environments. The space-alternating generalized expectation-maximization algorithm is applied to process the measurement data. Important statistical properties, such as average power delay profile, power azimuth profile, power elevation profile, root mean square delay spread, azimuth angular spread, elevation angular spread, and their cumulative distribution functions and correlation properties, are obtained and compared for different bands. New massive MIMO propagation properties, such as spherical wavefront, cluster birth-death, and non-stationarity over the antenna array, are validated for the four mmWave bands by investigating the variations of channel parameters. Two channel models are used to verify the measurements. The results indicate that massive MIMO effects should be fully characterized for mmWave massive MIMO systems.
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