arXiv subjects

Bo Wei

Publications and source records attributed to Bo Wei.

52 records · Page 3Linked to original sources

arXiv2020

Ergodic inventory control with diffusion demand and general ordering costs

In this work, we consider a continuous-time inventory system where the demand process follows an inventory-dependent diffusion process. The ordering cost of each order depends on the order quantity and is given by a general function, which is not even necessarily continuous and monotone. By applying a lower bound approach together with a comparison theorem, we show the global optimality of an $(s,S)$ policy for this ergodic inventory control problem.

math.OC↗

arXiv2020

Inbound Replenishment and Outbound Dispatch Decisions under Hybrid Shipment Consolidation Policies: An Analytical Model and Comparison

We consider a distribution warehouse where both the inbound inventory replenishment and outbound dispatch decisions are subject to fixed (as well as per-unit) transportation charges and demand is stochastic. In order to realize scale economies associated with transportation operations both on the outbound and inbound sides, dispatch schedules must be synchronized over time with inventory replenishment decisions. An immediate delivery policy on the outbound side does not make economic sense because outbound dispatch operations and costs will benefit from a temporal shipment consolidation policy. Our particular interest in this setting is the exact modeling and analysis of hybrid shipment consolidation policies, in comparison to the time- and quantity-based counterparts. Since shipment consolidation prolongs customer waiting and inventory holding, we investigate average delay penalty per order and average inventory per time unit as critical measures of performance of the distribution operation, along with the annual cost. By fixing the expected inbound replenishment and outbound dispatch frequencies, we compare these measures among the alternative ways of operation under hybrid, time-based, and quantity-based policies. This comparison then lends itself to an explicit analytical comparison of average costs under these three policies, without a need for solving the corresponding optimization problems.

math.OC↗

arXiv2020

Analytical Results on the Service Performance of Stochastic Clearing Systems

Stochastic clearing theory has wide spread applications in the context of supply chain and service operations management. Historical application domains include bulk service queues, inventory control, and transportation planning (e.g., vehicle dispatching and shipment consolidation). In this paper, motivated by a fundamental application in shipment consolidation, we revisit the notion of service performance for stochastic clearing system operation. More specifically, our goal is to evaluate and compare service performance of alternative operational policies for clearing decisions, as quantified by a measure of timely service referred as \emph{Average Order Delay} ($AOD$). All stochastic clearing systems are subject to service delay due to the inherent clearing practice, and $AOD$ can be thought of as a benchmark for evaluating timely service. Although stochastic clearing theory has a long history, existing literature on the analysis of $AOD$ as a service measure has several limitations. Hence, we extend the previous analysis by proposing a more general method for a generic analytical derivation of $AOD$ for any renewal-type clearing policy, including but not limited to alternative shipment consolidation policies in the previous literature. Our proposed method utilizes a new martingale point of view and lends itself for a generic analytical characterization of $AOD$, leading to a complete comparative analysis of alternative renewal-type clearing policies. Hence, we also close the gaps in literature on shipment consolidation via a complete set of analytically provable results regarding $AOD$ which were only illustrated through numerical tests previously.

math.OC↗

arXiv2020

WiEps: Measurement of Dielectric Property with Commodity WiFi Device -- An application to Ethanol/Water Mixture

WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet of Things (IoT) applications. Recently, most of these applications are based on analysis of the microwave reflections caused by physical movement of the objective. In this paper, a novel contactless wireless sensing technique named WiEps is developed to measure the dielectric properties of the material, exploiting the transmission characteristics of the WiFi signals. In WiEps, the material under test is placed between the transmitter antenna and receiver antenna. A theoretical model is proposed to quantitatively describe the relationship between CSI data and dielectric properties of the material. During the experiment, the phase and amplitude of the transmitted WiFi signals are extracted from the measured CSI data. The parameters of the theoretical model are calculated using measured data from the known materials. Then, WiEps is utilized to estimate the dielectric properties of unknown materials. The proposed technique is first applied to the ethanol/water mixtures. Then, additional liquids are measured for further verification. The estimated permittivities and conductivities show good agreement with the actual values, with the average error of 4.0% and 8.9%, respectively, indicating the efficacy of WiEps. By measuring the dielectric property, this technique is promising to be applied to new IoT applications using ubiquitous WiFi signals, such as food engineering, material manufacturing process monitoring, and security check.

eess.SP↗

arXiv2020

HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis

Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose HTMLPhish, a deep learning based data-driven end-to-end automatic phishing web page classification approach. Specifically, HTMLPhish receives the content of the HTML document of a web page and employs Convolutional Neural Networks (CNNs) to learn the semantic dependencies in the textual contents of the HTML. The CNNs learn appropriate feature representations from the HTML document embeddings without extensive manual feature engineering. Furthermore, our proposed approach of the concatenation of the word and character embeddings allows our model to manage new features and ensure easy extrapolation to test data. We conduct comprehensive experiments on a dataset of more than 50,000 HTML documents that provides a distribution of phishing to benign web pages obtainable in the real-world that yields over 93 percent Accuracy and True Positive Rate. Also, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language.

cs.CR↗

arXiv2020

A Randomized Nonlinear Rescaling Method in Large-Scale Constrained Convex Optimization

We propose a new randomized algorithm for solving convex optimization problems that have a large number of constraints (with high probability). Existing methods like interior-point or Newton-type algorithms are hard to apply to such problems because they have expensive computation and storage requirements for Hessians and matrix inversions. Our algorithm is based on nonlinear rescaling (NLR), which is a primal-dual-type algorithm by Griva and Polyak {[{Math. Program., 106(2):237-259, 2006}]}. NLR introduces an equivalent problem through a transformation of the constraint functions, minimizes the corresponding augmented Lagrangian for given dual variables, and then uses this minimizer to update the dual variables for the next iteration. The primal update at each iteration is the solution of an unconstrained finite sum minimization problem where the terms are weighted by the current dual variables. We use randomized first-order algorithms to do these primal updates, for which they are especially well suited. In particular, we use the scaled dual variables as the sampling distribution for each primal update, and we show that this distribution is the optimal one among all probability distributions. We conclude by demonstrating the favorable numerical performance of our algorithm.

math.OC↗

arXiv2020

A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification

Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals.

eess.AS↗

arXiv2019

Stochastic Clearing Systems with Multiple Input Processes

In this paper, we consider stochastic clearing systems with multiple drifted Brownian motion inputs. First, we propose an instantaneous rate policy, which is shown to be the optimal one among a large class of renewal type clearing policies in terms of average cost. Second, we propose a service measure about average weighted delay rate, and provide a unified method to calculate the service measure under different clearing policies. Moreover, we prove that under a fixed clearing frequency, the instantaneous rate policy outperforms a large class of clearing policies, and the instantaneous rate hybrid policy performs better than time-based policy, in terms of average weighted delay rate.

math.OC↗

arXiv2019

No Need of Data Pre-processing: A General Framework for Radio-Based Device-Free Context Awareness

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e. video-based and radio-based. Video-based applications can deliver good performance, but privacy is a serious concern. Radio-based context awareness has drawn researchers attention instead because it does not violate privacy and radio signal can penetrate obstacles. Recently, deep learning has been introduced into radio-based device-free context awareness and helps boost the recognition accuracy. The present works design explicit methods for each radio based application. They also use one additional step to extract features before conducting classification and exploit deep learning as a classification tool. The additional initial data processing step introduces unnecessary noise and information loss. Without initial data processing, it is, however, challenging to explore patterns of raw signals. In this paper, we are the first to propose an innovative deep learning based general framework for both signal processing and classification. The key novelty of this paper is that the framework can be generalised for all the radio-based context awareness applications. We also eliminate the additional effort to extract features from raw radio signals. We conduct extensive evaluations to show the superior performance of our proposed method and its generalisation.

cs.NI↗

arXiv2019

An Inexact Primal-Dual Algorithm for Semi-Infinite Programming

This paper considers an inexact primal-dual algorithm for semi-infinite programming (SIP) for which it provides general error bounds. To implement the dual variable update, we create a new prox function for nonnegative measures which turns out to be a generalization of the Kullback-Leibler divergence for probability distributions. We show that under suitable conditions on the error, this algorithm achieves an $\mathcal{O}(1/\sqrt{K})$ rate of convergence in terms of the optimality gap and constraint violation. We then use our general error bounds to analyze the convergence and sample complexity of a specific primal-dual SIP algorithm based on Monte Carlo integration. Finally, we provide numerical experiments to demonstrate the performance of our algorithm.

math.OC↗

arXiv2018

Corporative Stochastic Approximation with Random Constraint Sampling for Semi-Infinite Programming

We developed a corporative stochastic approximation (CSA) type algorithm for semi-infinite programming (SIP), where the cut generation problem is solved inexactly. First, we provide general error bounds for inexact CSA. Then, we propose two specific random constraint sampling schemes to approximately solve the cut generation problem. When the objective and constraint functions are generally convex, we show that our randomized CSA algorithms achieve an $\mathcal{O}(1/\sqrt{N})$ rate of convergence in expectation (in terms of optimality gap as well as SIP constraint violation). When the objective and constraint functions are all strongly convex, this rate can be improved to $\mathcal{O}(1/N)$.

math.OC↗

arXiv2018

From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern and the subjects do not have to carry a device on them. Recently, it has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments show that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. We are also the first to use complex-valued CSI to improve the performance in the environment with RFI.

cs.NI↗

arXiv2014

An improved vulnerability index of complex networks based on fractal dimension

With an increasing emphasis on network security, much more attention has been attracted to the vulnerability of complex networks. The multi-scale evaluation of vulnerability is widely used since it makes use of combined powers of the links' betweenness and has an effective evaluation to vulnerability. However, how to determine the coefficient in existing multi-scale evaluation model to measure the vulnerability of different networks is still an open issue. In this paper, an improved model based on the fractal dimension of complex networks is proposed to obtain a more reasonable evaluation of vulnerability with more physical significance. Not only the structure and basic physical properties of networks is characterized, but also the covering ability of networks, which is related to the vulnerability of the network, is taken into consideration in our proposed method. The numerical examples and real applications are used to illustrate the efficiency of our proposed method.

cs.SI↗

arXiv2014

Multi-fractal analysis of weighted networks

In many real complex networks, the fractal and self-similarity properties have been found. The fractal dimension is a useful method to describe fractal property of complex networks. Fractal analysis is inadequate if only taking one fractal dimension to study complex networks. In this case, multifractal analysis of complex networks are concerned. However, multifractal dimension of weighted networks are less involved. In this paper, multifractal dimension of weighted networks is proposed based on box-covering algorithm for fractal dimension of weighted networks (BCANw). The proposed method is applied to calculate the fractal dimensions of some real networks. Our numerical results indicate that the proposed method is efficient for analysis fractal property of weighted networks.

physics.soc-ph↗

arXiv2014

dRTI: Directional Radio Tomographic Imaging

Radio tomographic imaging (RTI) enables device free localisation of people and objects in many challenging environments and situations. Its basic principle is to detect the changes in the statistics of some radio quality measurements in order to infer the presence of people and objects in the radio path. However, the localisation accuracy of RTI suffers from complicated radio propagation behaviours such as multipath fading and shadowing. In order to improve RTI localisation accuracy, we propose to use inexpensive and energy efficient electronically switched directional (ESD) antennas to improve the quality of radio link behaviour observations, and therefore, the localisation accuracy of RTI. We implement a directional RTI (dRTI) system to understand how directional antennas can be used to improve RTI localisation accuracy. We also study the impact of the choice of antenna directions on the localisation accuracy of dRTI and propose methods to effectively choose informative antenna directions to improve localisation accuracy while reducing overhead. We evaluate the performance of dRTI in diverse indoor environments and show that dRTI significantly outperforms the existing RTI localisation methods based on omni-directional antennas.

cs.OH↗

arXiv2013

A new information dimension of complex networks

The fractal and self-similarity properties are revealed in many real complex networks. However, the classical information dimension of complex networks is not practical for real complex networks. In this paper, a new information dimension to characterize the dimension of complex networks is proposed. The difference of information for each box in the box-covering algorithm of complex networks is considered by this measure. The proposed method is applied to calculate the fractal dimensions of some real networks. Our results show that the proposed method is efficient for fractal dimension of complex networks.

cs.SI↗