Mengchu Zhou

New Jersey Institute of Technology | NJIT · Department of Electrical and Computer Engineering

Formulating a cooperative autonomous vehicle group is challenging in an urban scene that has complex road networks and diverse disturbance. Existing methods of vehicle cluster cooperation in a vehicular ad-hoc network cannot be applied to autonomous vehicles because the latter have different requirements for a vehicle group structure and communicat...

Knowledge distillation is a deep learning method that mimics the way that humans teach, i.e., a teacher network is used to guide the training of a student one. Knowledge distillation can generate an efficient student network to facilitate deployment in resource-constrained edge computing devices. Existing studies have typically mined knowledge from...

In today’s semiconductor manufacturing industry, wafer foundries often face the challenge of producing a variety of integrated circuit chip products using a single manufacturing line. To address this, multicluster tools have become a popular choice for processing multiple wafer types simultaneously. Operating such tools involves coordinating the ro...

As a crucial data preprocessing method in data mining, feature selection (FS) can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features. Evolutionary computing (EC) is promising for FS owing to its powerful search capability. However, in traditional EC-based met...

Convolutional neural networks (CNNs) have demonstrated excellent capability in various visual recognition tasks but impose an excessive computational burden. The latter problem is commonly solved by utilizing lightweight sparse networks. However, such networks have a limited receptive field in a few layers, and the majority of these networks face a...

To construct a strong classifier ensemble, base classifiers should be accurate and diverse. However, there is no uniform standard for the definition and measurement of diversity. This work proposes a learners' interpretability diversity (LID) to measure the diversity of interpretable machine learners. It then proposes a LID-based classifier ensembl...

Multitask optimization (MTO) is a new optimization paradigm that leverages useful information contained in multiple tasks to help solve each other. It attracts increasing attention in recent years and gains significant performance improvements. However, the solutions of distinct tasks usually obey different distributions. To avoid that individuals...

Cloud-edge hybrid systems are known to support delay-sensitive applications of contemporary industrial Internet of Things (IoT). While edge nodes (ENs) provide IoT users with real-time computing/network services in a pay-as-you-go manner, their resources incur cost. Thus, their profit maximization remains a core objective. With the rapid developmen...

Ultrasound imaging is widely used in medical diagnosis. It has the advantages of being performed in real time, cost-efficient, noninvasive, and nonionizing. The traditional delay-and-sum (DAS) beamformer has low resolution and contrast. Several adaptive beamformers (ABFs) have been proposed to improve them. Although they improve image quality, they...

With the advancement of science and technology in recent years, the rate of product upgrading by end users has increased, resulting in a high number of End-Of-Life (EOL) products. For environmental protection and economic benefits, disassembly lines are set up to recycle them. A disassembly line balancing problem arises and has attracted widespread...

In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and enables noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derive...

The conventional fuzzy C-means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image s...

We elaborate on a Kullback-Leibler divergence-based Fuzzy C-Means (FCM) algorithm by incorporating a tight wavelet frame transform and morphological reconstruction. To make membership degrees of each image pixel closer to those of its neighbors, a Kullback-Leibler divergence term on partition matrix is introduced as a part of FCM, thus resulting in...

We develop a residual-sparse Fuzzy C -Means (FCM) algorithm for image segmentation, which furthers FCM's robustness by realizing the favorable estimation of the residual (e.g., unknown noise) between an observed image and its ideal version (noise-free image). To achieve a sound tradeoff between detail preservation and noise suppression, morphologic...

Reachability is the basis for studying other dynamic properties of Petri nets (PNs). When a state equation is used to determine the reachability of a marking, we need to judge whether there is a corresponding legal firing sequence (LFS) for a non-negative integer solution (NIS), i.e., a firing count vector, of the state equation. The search for an...

Great efforts have been devoted to the intelligent control of autonomous systems. Yet, most of existing methods fail to effectively handle the uncertainty of their environment and models. Uncertain locations of dynamic obstacles pose a major challenge for their optimal control and safety, while their linearization or simplified system models reduce...

Dear Editor, In this letter, we analyze the public discourse sentiments over time and seek to understand the salient patterns around COVID-19 vaccines and vaccination from social media data. Globally, more than 373 million people have been diagnosed with COVID-19 and 5.66 million have died from this disease by 2022. It continues to have a negative...

This is a book.

Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and transferring knowledge from related source domains. In order to learn a discriminative and domain-invariant model, a critical step is to align source and target data well and thus reduce their distribution divergence. But existing DA methods mainly align the gl...

Distributed green cloud datacenters (DGCDs) are increasingly deployed around the world. DGCDs integrate many renewable sources to provide clean power and decrease their operating cost. They are spread over multiple locations, where renewable energy availability, bandwidth prices and grid electricity costs have high geographical diversity. This pape...

Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive performance in semi-supervised node classification. However, the existing methods are based on the a...

The lack of a sufficient number of reliable corners in low-textured environments is a big challenge for classical visual simultaneous localization and mapping (SLAM), especially for point feature-based methods. Many other features (i.e., line and plane segments) are often combined with points to restore an environmental structure. However, using su...

Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new u...

This work considers a task scheduling problem with deadline constraints in human-cyber-physical systems. To find its energy-efficient schedules in a short time, an autoencoder-embedded iterated local search algorithm is proposed to solve it. Iterated local search is selected as a main scheduler. In order to handle real-time requirements and high co...

With the rapid development of service computing, the demand for service recommendation is increasing. Quality of Service (QoS) prediction has been one of the key challenges for service recommendation. Existing deep learning-based methods have been proposed for QoS prediction, but further improvement of their neural network structures is still neede...

Product disassembly is critically important in recycling end-of-life products, reducing their negative impact on environmental pollution and minimizing resource waste. Disassembly line balancing problems have attracted much attention from researchers and industrial practitioners. Most of the existing studies, however, consider only human disassembl...

Accurately predicting Quality of Service (QoS) is one of the main challenges in the area of service recommendation, and it hasthusattractedmuchattention in recent years. This field existsmanymethods, most of which are inspired bycollaborative filtering in service recommendation. They predict the missing QoS values of services by collecting the hist...

To perform collaborative exploration tasks in outdoor environments, multi-robot systems require effective task planning and high-precision co-localization. However, there are many challenges in real-world environments, such as unavailable navigation maps and unpredictable obstacles. In this paper, we present a system architecture for autonomous mul...

Precise and real-time prediction of future network attacks can not only prompt cloud infrastructures to fast respond and protect network security, but also prevents economic and business losses. In recent years, neural networks, e.g., Bi-directional Gated Recurrent Unit network and Temporal Convolutional Network (TCN), have been proven to be suitab...

With the rapid changes and diversity of market demand, fabs have to produce wafers in many varieties and small batches. This brings a great challenge to the scheduling of wafer-residency-time-constrained cluster tools that concurrently process multiple types of wafers. Existing practice tends to employ all processing modules (PMs) of a required typ...

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder...

Credit card fraud detection (CCFD) is an important issue concerned by financial institutions. Existing methods generally employ aggregated or raw features as their representations to train their detection models. Yet such features tend to fall short of effectively exposing the characteristics of various frauds. In this work, we propose a spatial-te...

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds (also called balance) or a low transmission rate. To increase the success rate and...

With the rapid development of the Internet of Things (IoT), a large amount of data has been produced, and new requirements have been put forward for data mining. Clustering plays an essential role in discovering the underlying patterns of given IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, image proces...

Multi-label learning aims to solve classification problems where instances are associated with a set of labels. In reality, it is generally easy to acquire unlabeled data but expensive or time-consuming to label them, and this situation becomes more serious in multi-label learning as an instance needs to be annotated with several labels. Hence, sem...

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective whe...

The rising development of power systems and smart grids calls for advanced fault diagnosis techniques to prevent undesired interruptions and expenses. One of the most important part of such systems is transmission lines. This paper presents a survey on recent machine learning-based techniques for fault detection, classification, and location estima...

Resource allocation systems (RASs) belong to a kind of discrete event system commonly seen in the industry. In such systems, available resources are allocated to concurrently running processes to optimize some performance criteria. Search strategies in the reachability graph (RG) of a timed Petri net (PN) attracted much attention in the past decade...

This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space...

Since a noisy image has inferior characteristics, the direct use of Fuzzy C -Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM’s robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. To date, onl...

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus, automatically detect a fraudulent transaction. We...

In modern logistics and smart warehouse, a terminal exists as a hub to connect multiple transportation modes and exchange goods. Solving a block relocation problem (BRP) arising from block retrieval processes in a terminal is fundamentally important to enhance the terminal’s overall efficiency and save its energy. In this paper, we improve the stat...

Deep learning methods have shown great promise in high-voltage transmission lines’ (TLs’) intelligent inspections. The expansion of power systems, including TLs, has brought the problem of insulator fault detection into account more than before. In this article, a novel transfer learning framework based on a pretrained VGG-19 deep convolutional neu...

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and...

Recently, multimodal multiobjective optimization problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solu...

Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of...

Some recent research reveals that a topological structure in meta-heuristic algorithms can effectively enhance the interaction of population, and thus improve their performances. Inspired by it, we creatively investigate the effectiveness of using a scale-free network in differential evolution algorithm, and propose a scale-free network-based diffe...

Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time detection of transmission line faults and estimation of their locations. The expansion of power systems including transmission lines with various lengths have made the fault detection, classification, and location estimation process mor...

Digital finance has greatly facilitated people's lives, accelerated the circulation of capital in various fields, and enhanced the vitality of financial markets. However, it exposes many increasing risks and hidden dangers, such as stock volatility, trading fraud, privacy leakage, etc. In addition, the storage security and high-performance computin...

Text classification is a fundamental and important area of natural language processing for assigning a text into at least one predefined tag or category according to its content. Most of the advanced systems are either too simple to get high accuracy or centered on using complex structures to capture the genuinely required category information, whi...

With the development of Internet of Things (IoT), data are increasingly appearing at the edge of a network. Processing tasks at the network edge can effectively solve the problems of personal privacy leakage and server overloading. As a result, it has attracted a great deal of attention. A number of efficient convolutional neural network (CNN) mode...

This letter presents a deep learning-based prediction model for the quality-of-service (QoS) of cloud services. Specifically, to improve the QoS prediction accuracy of cloud services, a new QoS prediction model is proposed, which is based on multi-staged multi-metric feature fusion with individual evaluations. The multi-metric features include glob...

Extreme learning machine (ELM) is suitable for nonlinear soft sensor development. Yet it faces an over-fitting problem. To overcome it, this work integrates bound optimization theory with Variational Bayesian (VB) inference to derive novel L1 norm-based ELMs. An L1 term is attached to the squared sum cost of prediction errors to formulate an object...

Green Data Centers (GDCs) are more and more deployed world-wide. They integrate many renewable sources to provide clean power and decrease their operating cost. GDCs are typically deployed over multiple locations where renewable energy availability, bandwidth prices and grid electricity cost have high geographical diversity. This paper focuses on d...

This paper aims to present a comprehensive survey on water quality soft-sensing of a wastewater treatment process (WWTP) based on artificial neural networks (ANNs). We mainly present problem formulation of water quality soft-sensing, common soft-sensing models, practical soft-sensing examples and discussion on the performance of soft-sensing models...

This study proposes an approach to the construction of granular models directly based on information granules expressed both in input and output spaces. Associating these information granules, the constructed granular models come in the framework of three layers networks: input granules, an inference scheme and output granules. The proposed approac...

With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as their transactional representations, while they fail to reveal the hidden fraudulent behaviors. In this...

Resource allocation systems (RASs) exist in various fields of modern society. The deadlock control problem is a crucial issue in control theory of RAS. This work is concentrated on a special class of shared resource and process-oriented Petri nets whose initial marking can have only a token in every resource place. Using mixed-integer programming (...

From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown...

The recent development of channel technology has promised to reduce the transaction verification time in blockchain operations. When transactions are transmitted through the channels created by nodes, the nodes need to cooperate with each other. If one party refuses to do so, the channel is unstable. A stable channel is thus required. Because nodes...

In mobile interactive systems, there exist several components that can move and interact with each other. The current methods fail to give a comprehensive description of their properties and have inadequate modeling and analysis capacity for them. This paper concludes three system properties called system connectivity, interaction soundness and dat...

Feature selection can be used to solve imbalanced classification problems encountered in big data projects. There often exist multiple feature subsets achieving the same accuracy. These subsets tend to exhibit different acquisition difficulty and reliability, thus offering decision-makers with multiple choices if they can be well-identified. This w...

In this paper, we propose and evaluate the idea that data need to be preconditioned prior to compression, such that they can better match the design philosophies of lossy compressors for HPC scientific data. In particular, we aim to identify a reduced model that can be utilized to transform the original data into a more compressible form. We begin...

Accurately processing dynamic evolution events is extremely challenging for autonomous vehicle groups in an urban scene, which can be disturbed by manned vehicles, roadside obstacles, traffic lights, and pedestrians. Existing work focuses on a dynamic evolution method for such groups in a highway scene only. Its outcomes cannot be directly used to...

For tracking suspicious objects using intelligent robots, Multiple Object Tracking (MOT) has gained great attention. MOT is easily affected by long-term severe occlusion. This work proposes a joint MOT algorithm to handle such occlusion. Pairs of frames in complicated environments are taken as input. A center-based feature extraction framework is d...

This paper considers a stochastic parallel machine scheduling problem in a just-in-time manufacturing context, in which its processing time can be described by a gamma or log-normal distribution. In order to obtain a high-performance schedule in a reasonable time, this work proposes a two-stage genetic algorithm with optimal computing budget alloca...

A colored traveling salesman problem (CTSP) is a path optimization problem in which colors are used to characterize diverse matching relationship between cities and salesmen. Namely, each salesman has a single color while every city has one to multiple salesmen’s colors, thus allowing salesmen to visit exactly once the cities of their colors. It is...

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer revi...

Width deviation is an important metric for evaluating the quality of a hot-rolled strip in steel production systems. This paper considers a width deviation prediction problem and proposes a Machine-learning and Genetic-algorithm-based Hybrid method named MGH to obtain a prediction model. Existing work mainly focuses on high prediction accuracy, whi...

To overcome long propagation delays for data exchange between the remote cloud data center and end devices in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) is emerging to push mobile computing, network control and storage to the network edges. A cloudlet in MEC is a mobility-enhanced small-scale cloud, which contains several MEC servers...

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In it, each city has one to multiple colors and allows a salesman in the same color to visit exactly once. This work presents for the first time a CTSP whose edge weights among the cities change over time. It can be applied to dyna...

A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users' expectations on both aspects. To tackle this challenge, this wor...