PLA Army Engineering University

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

Methods for aggregating interval-valued intuitionistic fuzzy information are investigated.Some operational laws of intervalvalued intuitionistic fuzzy numbers are defined.Based on these operational laws,some aggregation operators,including interval-valued intuitionistic fuzzy weighted arithmetic aggregation operator and interval-valued intuitionistic fuzzy weighted geometric aggregation operator,are proposed.The score function and accuracy function of interval-valued intuitionistic fuzzy number are defined,and based on these two functions,a method for ranking interval-valued intuitionistic fuzzy numbers is presented.Finally,an approach for decision making with interval-valued intuitionistic fuzzy information is developed,and a practical example is provided to illustrate the developed approach.

5G cellular networks are assumed to be the key enabler and infrastructure provider in the ICT industry, by offering a variety of services with diverse requirements. The standardization of 5G cellular networks is being expedited, which also implies more of the candidate technologies will be adopted. Therefore, it is worthwhile to provide insight into the candidate techniques as a whole and examine the design philosophy behind them. In this article, we try to highlight one of the most fundamental features among the revolutionary techniques in the 5G era, i.e., there emerges initial intelligence in nearly every important aspect of cellular networks, including radio resource management, mobility management, service provisioning management, and so on. However, faced with ever-increasingly complicated configuration issues and blossoming new service requirements, it is still insufficient for 5G cellular networks if it lacks complete AI functionalities. Hence, we further introduce fundamental concepts in AI and discuss the relationship between AI and the candidate techniques in 5G cellular networks. Specifically, we highlight the opportunities and challenges to exploit AI to achieve intelligent 5G networks, and demonstrate the effectiveness of AI to manage and orchestrate cellular network resources. We envision that AI-empowered 5G cellular networks will make the acclaimed ICT enabler a reality.

Our manuscript was based on surveillance cases of COVID-19 identified before January 26, 2020. As of February 20, 2020, the total number of confirmed cases in mainland China has reached 18 times of the number in our manuscript. While the methods and the main conclusions in our original analyses remain solid, we decided to withdraw this preprint for the time being, and will replace it with a more up-to-date version shortly. Should you have any comments or suggestions, please feel free to contact the corresponding author.

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

The intuitionistic fuzzy set has shown definite advantages in handling vagueness and uncertainty over a fuzzy set. Taking the powerfulness of the analytic hierarchy process (AHP) and the fuzzy AHP (FAHP) into account when tackling comprehensive multi-criteria decision-making problems, in this paper, we extend the classic AHP and the FAHP into the intuitionistic fuzzy AHP (IFAHP) in which the preferences are represented by intuitionistic fuzzy values. The IFAHP can be used to handle more complex problems, where the decision maker has some uncertainty in assigning preference values to the objects considered. The paper proposes a new way to check the consistency of an intuitionistic preference relation and then introduces an automatic procedure to repair the inconsistent one. It is worth pointing out that our proposed method can improve the inconsistent intuitionistic preference relation without the participation of the decision maker, and thus, it can save much time and show some advantages over the AHP and the FAHP. This paper also develops a novel normalizing rank summation method to derive the priority vector of an intuitionistic preference relation, on which the priorities of the hierarchy in the IFAHP are derived. The procedure of the IFAHP is given in detail, and an example concerning global supplier development is used to demonstrate our results.

In this letter, we investigate whether the use of artificial noise (AN) is helpful to enhance the secrecy rate of an intelligent reflecting surface (IRS) assisted wireless communication system. Specifically, an IRS is deployed nearby a single-antenna receiver to assist in the transmission from a multi-antenna transmitter, in the presence of multiple single-antenna eavesdroppers. Aiming to maximize the achievable secrecy rate, a design problem for jointly optimizing transmit beamforming with AN or jamming and IRS reflect beamforming is formulated, which is however difficult to solve due to its non-convexity and coupled variables. We thus propose an efficient algorithm based on alternating optimization to solve the problem sub-optimally. Simulation results show that incorporating AN in transmit beamforming is beneficial under the new setup with IRS reflect beamforming. In particular, it is unveiled that the IRS-aided design without AN even performs worse than the AN-aided design without IRS as the number of eavesdroppers near the IRS increases.

A hesitant fuzzy set, allowing the membership of an element to be a set of several possible values, is very useful to express people's hesitancy in daily life. In this paper, we define the distance and correlation measures for hesitant fuzzy information and then discuss their properties in detail. These measures are all defined under the assumption that the values in all hesitant fuzzy elements (the fundamental units of hesitant fuzzy sets) are arranged in an increasing order and two hesitant fuzzy elements have the same length when we compare them. We can find that the results, by using the developed distance measures, are the smallest ones among those when the values in two hesitant fuzzy elements are arranged in any permutations. In addition, the derived correlation coefficients are based on different linear relationships and may have different results. © 2011 Wiley Periodicals, Inc.

The generalized ordered weighted averaging (GOWA) operators are a new class of operators, which were introduced by Yager (Fuzzy Optim Decision Making 2004;3:93–107). However, it seems that there is no investigation on these aggregation operators to deal with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. In this paper, we first develop some new generalized aggregation operators, such as generalized intuitionistic fuzzy weighted averaging operator, generalized intuitionistic fuzzy ordered weighted averaging operator, generalized intuitionistic fuzzy hybrid averaging operator, generalized interval-valued intuitionistic fuzzy weighted averaging operator, generalized interval-valued intuitionistic fuzzy ordered weighted averaging operator, generalized interval-valued intuitionistic fuzzy hybrid average operator, which extend the GOWA operators to accommodate the environment in which the given arguments are both intuitionistic fuzzy sets that are characterized by a membership function and a nonmembership function, and interval-valued intuitionistic fuzzy sets, whose fundamental characteristic is that the values of its membership function and nonmembership function are intervals rather than exact numbers, and study their properties. Then, we apply them to multiple attribute decision making with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. © 2009 Wiley Periodicals, Inc.

Fractional derivative has a history as long as that of classical calculus, but it is much less popular than it should be. What is the physical meaning of fractional derivative? This is still an open problem. In modeling various memory phenomena, we observe that a memory process usually consists of two stages. One is short with permanent retention, and the other is governed by a simple model of fractional derivative. With the numerical least square method, we show that the fractional model perfectly fits the test data of memory phenomena in different disciplines, not only in mechanics, but also in biology and psychology. Based on this model, we find that a physical meaning of the fractional order is an index of memory.

Hesitant fuzzy linguistic term sets (HFLTSs) are used to deal with situations in which the decision makers (DMs) think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. Compared with fuzzy linguistic approaches, they are more convenient and flexible to reflect the DMs' preferences in decision making. For further applications of HFLTSs to decision making, we develop a concept of hesitant fuzzy linguistic preference relations (HFLPRs) as a tool to collect and present the DMs' preferences. Due to the importance of the consistency measures using preference relations in decision making, we develop some consistency measures for HFLPRs to ensure that the DMs are being neither random nor illogical. A consistency index is defined to establish the consistency thresholds of HFLPRs to measure whether an HFLPR is of acceptable consistency. For HFLPRs with unacceptable consistency, we develop two optimization methods to improve the consistency until they are acceptable. Several illustrative examples are given to validate the consistency measures and the optimization methods.

In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA)-based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array, we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit the NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function-based beamforming (BF) scheme to obtain the BF weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works.

A self-assembled spongelike (S) ultralight (<italic>ρ</italic> ≈ 140 mg cm⁻³) aerogel was fabricated through polypyrrole (PPy) and reduced graphene oxide (RGO).

Electromagnetic wave absorbing materials that can exhibit effective absorption in a broad bandwidth at a thin thickness are strongly desired due to their widespread applications in electronic devices. In this study, hybrids of MoS2 and reduced graphene oxide (RGO) were prepared and their microwave absorption performance was investigated for the first time. It was found that a thin sample consisting of 10 wt % MoS2/RGO hybrid in the wax matrix exhibited an effective microwave absorption bandwidth of 5.72 GHz at the thickness less than 2.0 mm. The highest reflection loss of -50.9 dB was observed at 11.68 GHz for a sample with a thickness of 2.3 mm. Results obtained in this study indicate that hybrids of MoS2 and RGO are promising microwave absorbing materials, which can exhibit broad effective absorption bandwidth at low filler loading and thin thickness.

Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance, and numerous public services. However, the deployment of amateur drones poses various safety, security, and privacy threats. To cope with these challenges, amateur drone surveillance has become a very important but largely unexplored topic. In this article, we first present a brief survey to show the state-of-the-art studies on amateur drone surveillance. Then we propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in detail, accompanied by the technical challenges and open issues. Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over.

This paper investigates the physical layer security of a satellite network, whose downlink spectral resource is shared with a terrestrial cellular network. We propose to employ a multi-antenna base station (BS) as a source of green interference to enhance secure transmission in the satellite network. By taking the mutual interference between these two networks into account, we first formulate a constrained optimization problem to maximize the instantaneous rate of the terrestrial user while satisfying the interference probability constraint of the satellite user. Then, with the assumption that imperfect channel state information (CSI) and statistical CSI of the link between the BS and satellite user are available at the BS, we present two beamforming (BF) schemes, namely, hybrid zero-forcing and partial zero-forcing to solve the optimization problem and obtain the BF weight vectors in a closed form. Moreover, we analyze the secrecy performance of primary satellite network by considering two practical scenarios, namely: Scenario I, the eavesdroppers CSI is unknown at the satellite and Scenario II, the eavesdroppers CSI is known at the satellite. Specifically, we derive the analytical expressions for the secrecy outage probability for Scenario I and the average secrecy rate for Scenario II. Finally, numerical results are provided to confirm the superiority of the proposed BF schemes and the validity of the performance analysis, as well as demonstrate the impacts of various parameters on the secrecy performance of the satellite network.

The power-average (PA) operator and the power-ordered-weighted-average (POWA) operator are the two nonlinear weighted-average aggregation tools whose weighting vectors depend on the input arguments. In this paper, we develop a power-geometric (PG) operator and its weighted form, which are on the basis of the PA operator and the geometric mean, and develop a power-ordered-geometric (POG) operator and a power-ordered-weighted-geometric (POWG) operator, which are on the basis of the POWA operator and the geometric mean, and study some of their properties. We also discuss the relationship between the PA and PG operators and the relationship between the POWA and POWG operators. Then, we extend the PG and POWG operators to uncertain environments, i.e., develop an uncertain PG (UPG) operator and its weighted form, and an uncertain power-ordered-weighted-geometric (UPOWG) operator to aggregate the input arguments taking the form of interval of numerical values. Furthermore, we utilize the weighted PG and POWG operators, respectively, to develop an approach to group decision making based on multiplicative preference relations and utilize the weighted UPG and UPOWG operators, respectively, to develop an approach to group decision making based on uncertain multiplicative preference relations. Finally, we apply both the developed approaches to broadband Internet-service selection.

Highlights Metal–organic frameworks (MOFs) are used to directly initiate the gelation of graphene oxide (GO), producing MOF/rGO aerogels. The ultralight magnetic and dielectric aerogels show remarkable microwave absorption performance with ultralow filling contents. Abstract The development of a convenient methodology for synthesizing the hierarchically porous aerogels comprising metal–organic frameworks (MOFs) and graphene oxide (GO) building blocks that exhibit an ultralow density and uniformly distributed MOFs on GO sheets is important for various applications. Herein, we report a facile route for synthesizing MOF/reduced GO (rGO) aerogels based on the gelation of GO, which is directly initiated using MOF crystals. Free metal ions exposed on the surface of MIL-88A nanorods act as linkers that bind GO nanosheets to a three-dimensional porous network via metal–oxygen covalent or electrostatic interactions. The MOF/rGO-derived magnetic and dielectric aerogels Fe 3 O 4 @C/rGO and Ni-doped Fe 3 O 4 @C/rGO show notable microwave absorption (MA) performance, simultaneously achieving strong absorption and broad bandwidth at low thickness of 2.5 (− 58.1 dB and 6.48 GHz) and 2.8 mm (− 46.2 dB and 7.92 GHz) with ultralow filling contents of 0.7 and 0.6 wt%, respectively. The microwave attenuation ability of the prepared aerogels is further confirmed via a radar cross-sectional simulation, which is attributed to the synergistic effects of their hierarchically porous structures and heterointerface engineering. This work provides an effective pathway for fabricating hierarchically porous MOF/rGO hybrid aerogels and offers magnetic and dielectric aerogels for ultralight MA.

Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. Despite decades of effort and many achievements, there are still challenges in feature extraction and model design. In this paper, we review the advancement in image segmentation methods systematically. According to the segmentation principles and image data characteristics, three important stages of image segmentation are mainly reviewed, which are classic segmentation, collaborative segmentation, and semantic segmentation based on deep learning. We elaborate on the main algorithms and key techniques in each stage, compare, and summarize the advantages and defects of different segmentation models, and discuss their applicability. Finally, we analyze the main challenges and development trends of image segmentation techniques.

We investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs), using game theoretic solutions. To cope with the lack of centralized control and local influences, we propose two special cases of local interaction game to study this problem. The first is local altruistic game, in which each user considers the payoffs of itself as well as its neighbors rather than considering itself only. The second is local congestion game, in which each user minimizes the number of competing neighbors. It is shown that with the proposed games, global optimization is achieved with local information. Specifically, the local altruistic game maximizes the network throughput and the local congestion game minimizes the network collision level. Also, the concurrent spatial adaptive play (C-SAP), which is an extension of the existing spatial adaptive play (SAP), is proposed to achieve the global optimum both autonomously as well as rapidly.