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Truck congestion at chemical sites is a persistent problem that is difficult to solve, even using a truck appointment system. This study presents an alternative solution to improve the flexibility of chemical sites by creating a drop-swap terminal adjacent to the site location. The terminal serves as an intermediate depot where the trucks can drop empty containers and swap them with preloaded containers without entering the site. This study aims to evaluate the performance of such a solution in mitigating truck congestion at chemical sites. The problem is modeled as a nonstationary semi-open queueing network with time-varying arrivals. We propose a combination of a fluid-flow approximation and a decomposition-aggregation method to estimate the time-dependent performance of the system. A chemical site in the Netherlands is presented as a case study. Several scenarios are tested and evaluated. Numerical results show that a drop-swap terminal can effectively reduce truck idling time and increase logistics efficiency at chemical sites. We also found that swapping containers on chassis is a cheaper and greener option to operate the terminal. However, the investment needed to support the operation should not be overlooked to sustain the benefits. The study concluded with several key messages for site operators who wish to maximize the benefit from a drop-swap terminal.

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0981 .

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24x speedup compared to conventional uncoded MapReduce, a 12% to 52% reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes., 13 pages, 4 figures

Virtual power plants (VPPs) offer a cost-effective solution to incentivize coordination between different resources participating in joint energy and reserve markets. However, emerging technologies such as storage and demand response cannot deliver flexibility over long periods due to inherent energy limitations. In this article, we, therefore, inform the day-ahead scheduling of VPPs with forecast scenarios of the balancing stage, which are complemented with information on nonshiftable load, renewable generation, and electricity prices. These multivariate scenarios (incorporating both time and cross-variable dependencies) are obtained using a machine learning framework in which probabilistic forecasts are converted into time trajectories using a copula-based sampling. The model is enriched with a detailed representation of the intraday decision stage wherein all flexible resources are dynamically allocated over the daily horizon. To ensure the reliability of the solution, we take into full consideration the revenues and unit-specific costs related to the activation of reserves. Outcomes show that improving the representation of the intraday dispatch stage while relying on representative balancing uncertainties are two complementary components for increasing the quality of the VPP day-ahead strategy, which ultimately fosters its economic value.

Cloud deployment architectures have become a preferable computation model of Big Data (BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a such deployment model, the data are no longer physically maintained under the user’s direct control, which raises new security concerns. In this context, BD security plays a decisive role in the widespread adoption of cloud architectures. However, it is challenging to develop a comprehensive security plan unless it is based on a preliminary analysis that ensures a realistic secure assembly and addresses domain-specific vulnerabilities. This article presents a novel security-by-design framework for BD frameworks deployment over cloud computing (BigCloud). In particular, it relies on a systematic security analysis methodology and a completely automated security assessment framework. Our framework enables the mapping of BigCloud security domain knowledge to the best practices in the design phase. We validated the proposed framework by implementing an Apache Hadoop stack use case. The study findings demonstrate its effectiveness in improving awareness of security aspects and reducing security design time. It also evaluates the strengths and limitations of the proposed framework, from which it highlights the main existing and open challenges in the BigCloud-related area.

This paper proposes a new concept of Aviation-to-Grid (A2G) that utilizes electric aircraft (EA) charging to provide flexibility to the power grid. Smart EA charging system with battery swap method is developed using PV, gas turbine, and grid electricity. Hourly energy dispatch strategy is produced based on the mixed integer linear programming method to meet electrified aviation and provide A2G frequency response. Case studies are conducted in 8 major UK airports considering seasonal flight schedules and power system scenarios. Results show that the EA charging system can provide effective primary and secondary frequency response to improve the frequency nadir by 0.2 - 0.3 Hz under grid disturbance. The total A2G frequency response capacity across the 8 UK airport can reach up to 1,300 MW overnight and up to 900 MW daytime. Annual A2G frequency response revenue is 46.58 million pounds, which can cover 19.8% to 30% of EA charging costs.

Existing programming practices for building Android apps mainly follow the one-size-fits-all strategy to include lots of functions and adapt to most types of devices. However, this strategy can result in software bloat and many serious issues, such as slow download speed, and large attack surfaces. Existing solutions cannot effectively debloat an app as they either lack flexibility or require human efforts. This work proposes a novel feature-oriented debloating approach and builds a new tool, named XDebloat, to automate this process in a flexible manner. First, XDebloat supports feature location approaches at a fine granularity. It also makes the feature location results editable and changeable. Second, XDebloat considers several Android-oriented issues (i.e., callback, UI dependencies), which are not covered in the state-of-art approaches. Third, XDebloat support two major debloating strategies: pruning-based and module-based debloating. We evaluate XDebloat with 200 open-source and 1,000 commercial apps. The results show that XDebloat can successfully remove unwanted features from apps or transform them into on-demand modules within 10 minutes. For the pruning-based debloating strategy, on average, XDebloat can remove 32.1% code from an app. For the module-based debloating strategy, XDebloat can help developers build modules automatically.

Traditional data sharing systems are facing new challenges when implementing access control with more and more complex data sharing requirements. Flexibility of user revocation in completely decentralized environments needs to be taken into account. In this paper, we propose a Key-Policy Attribute-Based Encryption scheme with Multiple Authorities and Flexible Revocation (MAFR-KP-ABE) to achieve the features of decentralized authorization and flexible revocation. We prove the security of our MAFR-KP-ABE scheme in the standard model and provide the comparison with relevant schemes to demonstrate its efficiency. Then we propose a fine-grained access control system based on MAFR-KP-ABE scheme and blockchain that matches the need of paid data sharing services with several security properties enhanced. Security analysis and system implementation are given subsequently to demonstrate our system efficient and secure.

The Internet of Things (IoT) aids an interconnection between systems, humans, and services to develop computation-intensive autonomous applications. Service-oriented architecture (SOA) concepts have been used as defacto software architecture to develop IoT-based systems. Though SOA facilitates various benefits, several challenges exist for integration with IoT-based systems, including configurability, interoperability, and manageability. The ontology-based approach provides a semantically enriched and rigorous platform for interoperating different heterogeneous IoT-based systems and applications. The semantic modelling research has focused on IoT resource management without concerning the method of accessing and utilizing IoT information. In this context, an IoT-based large-scale SOA (IoT-LSS) ontology is proposed that supports interoperability, heterogeneity, flexibility, manageability, extendibility, scalability. This ontology integrates the concept of large-scale SOA (LSS) with semantic sensor network (SSN) ontology. Clinical decision support system (CDSS) ontology has considered as a case study to illustrate the proposed ontology. A detailed comparative analysis between the proposed system and several existing IoT-based service ontologies has been performed based on different characteristics. This analysis shows that IoT-LSS supports dynamic features and exposes to service with its different mechanisms. This article presents an ontological framework for the IoT service-based environment to efficiently use for service composition mechanisms in the large service domain.

Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence., Comment: Accepted to IEEE Transactions on Software Engineering (Aug 2021); This author preprint is 26 pages, the final IEEE version is 25 pages due to reformatting, content is same. More details: https://rashina.com/

The Internet of Things (IoT) has been growing over the past few years due to its flexibility and ease of use in real-time applications. The IoT’s foremost task is ensuring that there is proper communication between different types of applications and devices, and that the application layer protocols fulfill this necessity. However, as the number of applications grows, it is necessary to modify or enhance the application layer protocols according to specific IoT applications, allowing specific issues to be addressed, such as dynamic adaption to network conditions and interoperability. Recently, several IoT application layer protocols have been enhanced and modified according to application requirements. However, no existing survey articles have focused on these protocols. In this article, we survey traditional and recent advances in IoT application layer protocols, as well as relevant real-time applications and their adapted application layer protocols for improving performance. As changing the nature of protocols for each application is unrealistic, machine learning offers means of making protocols intelligent and able to adapt dynamically. In this context, we focus on providing open challenges to drive IoT application layer protocols in such a direction.

Permanent magnet linear synchronous motor (PMLSM) has a wide application prospect in transmission systems of automatic production, logistics and other fields. However, the existing PMLSM has the problems of poor flexibility and large thrust fluctuation adapted to load and stroke variation. In order to solve this problem, a dual-modular PMLSM is proposed. The motor can be flexibly configured with multiple-unit-primary (MUP) and multiple-unit-secondary (MUS), and achieve low thrust fluctuation. The matching criterion of unit-primary (UP) and secondary is established to reveal the relationship between the slot/pole combination and the winding utilization. The rules of the average thrust varying with the different UP and unit-secondary (US) lengths are illustrated. Then, the matching criterion of different UP is proposed, and the optimization method of the spacing between UP (S1) is obtained by establishing the end force model. The design standard of the optimal S1 is obtained by combining the two factors of minimum detent force and maximum thrust. The results are verified by the simulation and experiments on a 3UP-96US dual-modular PMLSM circular transmission line system and its prototype.

IaaS from the public cloud is becoming a new option for organizations in need of HPC capabilities. IaaS offers greater flexibility in hardware choices and operational costs. Furthermore, IaaS enables small organizations to access resources previously reserved for organizations with larger capitals. This study uses the HPCG benchmark to assess the performance of parallel solvers, which are critical in computational engineering, and microbenchmarks to measure collective MPI operations. The benchmarks encompass IaaS from five cloud vendors (AWS, Google Cloud Platform, Azure, Oracle CIoud Infrastructure, Packet), and two architectures, ARM and x86_64. The benchmarks cover clusters with up to 4,500 cores and illustrate the benefits of higher network bandwidth when scaling up clusters. The results for some of the clusters are particularly promising as they exhibit good scalability and compare well to on-premises supercomputers. Additionally, the study includes a preliminary cost estimate based on on-demand prices for IaaS computational power and memory.

A duty of loyalty prohibits fiduciaries from appropriating business opportunities from their companies. Starting in 2000, Delaware, followed by several other states, allowed boards to waive their duty. We show that public firms covered by waiver laws invest less in R&D, produce fewer and less valuable patents, and exhibit abnormally high inventor departures. Remaining innovation activities contribute less to firm value, a fact confirmed by the market reaction when firms reveal their curtailed internal growth opportunities by announcing acquisitions. Consistent with the laws’ intent to provide contracting flexibility to emerging firms, we find evidence of positive impacts for small firms. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Resilience and flexibility are the means by which a network stands for an efficient service provider platform. Cognitive packet network (CPN) is such a paradigm that solely depends on the behavioral and intelligence capacity of the packets that carry valuable information within the network. Unlike conventional networks, CPN relies on the decision-making ability of the packets. In CPN, different types of packets take part in finding the optimal route from the source to destination. Further, packets dynamically adapt environmental changes by using recurrent neural network-based learning algorithms. Mapping cognitive features along with enrichment of quality of service is the main task of CPN. Due to continuous updates in the packet information vulnerable elements are found to be less prone to attack the CPN, thus resulting in a robust networking solution. In this paper we firstly present a novel taxonomic structure of the CPN. Then, we discuss the stringent routing techniques normally used in CPN. We also investigate various adaptive aware features in CPN. Next, we present intuitive emergency service and sensor network design prospects of the CPN. The article also discusses various means of cognitive aware mitigation aligned with the packets in CPN. Finally, we deliver how CPN is secure and available security aspects.

Due to the resource-constraint nature and lack of lightweight computing solutions for diagnostic devices in healthcare IoT, provisioning time-critical responses is still challenging. In this paper, we propose DC-Health, a Dew Computing enabled IoT healthcare solution for offline and ultra-low latency decisions. The proposed solution connects a large number of healthcare devices and provisions user-specific services even when Internet connectivity is not available. The computation module is placed at the extreme edge rather than the cloud to reduce the complexity and to improve the user-specific services. In addition to the other computing facilities provided by the cloud, fog, and edge, our solution performs with a negligible dependency on the Internet. We develop a prototype of DC-Health, which monitors the heart condition using the ECG sensors with end-mile services, flexibility in terms of user-control, and mobility feature. The experimental implementations show that the proposed architecture minimizes the network response time by at least 92% and 98%, compared to the fog and cloud-based approaches, respectively. Along with this, the proposed technique also reduces the CPU and memory usages, and response time by around 30% compared to the conventional method.

Genomic profiling technologies have enabled the development of targeted therapies designed to target specific biomarkers and molecular pathways involved in the pathophysiology of tumor initiation, metastasis, and drug resistance. In recent years, clinical trials with innovative design focus on the development of novel agents based on specific patient molecular alterations or other tumor characteristics and include patients with heterogenous tumor types. Precision oncology studies with innovative design associated with novel dose-finding approaches and data analysis focusing on subgroups of patients are characteristic of master protocols. Real-world data, patient-reported outcomes, and N-of-1 trials enhance the knowledge base of evidence to deliver personalized treatment to patients. Master protocols accelerate drug development by enabling simultaneous multiple sub-studies that match the patient's tumor molecular profile with experimental treatment arms. However, the increased flexibility of precision oncology trials is often associated with small subpopulations of patients, which may be underpowered to draw statistically robust conclusions. Despite their limitations, innovative clinical trials continue to rapidly translate the emerging discoveries of novel drugs into unprecedented clinical outcomes in patients with cancer and to accelerate the implementation of precision oncology.

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to given chemical composition and application. The extreme flexibility of machine learning models promises to yield reactive force fields that provide a more general description of chemical bonding. However, a significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely-used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance and improved learning rates when compared to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable reactive force fields.

With the promising advances in the telecommunication and automotive industries, there are increasing concerns on the reliability of Intelligent Transportation Systems (ITS) applications and standardization efforts. Nowadays, vehicles are empowered with innovative communications and sensing capabilities to accelerate next-generation Connected and Autonomous Vehicles (CAV) or Vehicle-to-Everything (V2X) communications. Furthermore, V2X is based on many radio access technologies including Dedicated Short-Range Communications (DSRC), Cellular-V2X, and millimeter-wave. It is expected that a single technology is incapable to support the diverse QoS and reliability requirements of vehicular safety and non-safety applications. Therefore, recent research works have investigated and developed different approaches to enable multi-radio access technologies (Multi-RAT) in V2X networks to support these multitudes of requirements. In addition, the heterogeneity of diverse radio access technologies and rigidity in their deployment increases the complexity of the network management. Complications may also arise from inefficient resource utilization due to the complexity of an optimum RAT selection or multi-link establishment through Multi-RATs. Therefore, a global monitoring paradigm such as Software-Defined Vehicular Network (SDVN) has been identified as a solution to reduce this bottleneck, by adding flexibility and agility to the vehicular network. This paper extensively reviews recent related works on Multi-RAT V2X networks focusing on the opportunistic selection mechanism of RAT, allocation of radio resources, and simultaneous establishment of multi-link over a heterogeneous network. Consequently, we have outlined the SDVN architecture, layers, features, and significance towards enhancing the performance of such Multi-RAT networks. Finally, several potential challenges with probable solutions and opportunities are discussed.

To reinforce competitiveness, companies must focus on competitive priorities including cost, quality, delivery, and flexibility. The competitiveness also depends on the adherence to environmental requirements and the ability of companies to survive unforeseen disruptions. Lean, agile, resilient, and green (LARG) paradigm is a hybrid supply chain paradigm, which can enhance competitiveness in today's complex markets. In this article, we propose a new conceptual decision-making model to evaluate LARG supply chain performance in medical equipment companies. In this regard, supply chain operations reference model attributes were utilized as performance evaluation criteria. Furthermore, the appropriate practices of LARG supply chain are selected through fuzzy Delphi method. Also, an extension of decision making trial and evaluation laboratory-based analytic network process method is proposed to uncover network relationships and obtain the weights for dimensions and elements. According to the results, the agile paradigm, responsiveness, and quick introduction of new products are determined as the most prioritized elements in their corresponding dimensions, which emphasize the importance of the customer satisfaction. Finally, the extended hesitant fuzzy linguistic VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) is applied to rank LARG performance of Iranian needles and syringes companies via qualitative group decision-making process. The results of this study are conformed to conventional selection results of needles and syringes suppliers conducted by Iran's Ministry of Health.

Computational experiments are an important method for carrying out the quantitative analysis of complex systems and play a major role in mapping the real world to the virtual world. However, the flexibility of computational experiments leads to arbitrary modeling processes and unconvincing results, which greatly hinder the large-scale application of this method. In this context, the verification of computational models has become an urgent problem in this field. Currently, model evaluation is still in its infancy and the existing evaluation methods are not mature enough. Thus, we took epidemic models as the research object and proposed a capability maturity evaluation framework for computational models of artificial society. The framework differs from previous assessment methods that focus on the validity of results, but instead provides a comprehensive evaluation from two perspectives: 1) evaluation of the model itself--by comparing the expectation with the final implementation, we can obtain whether the model meets the expectation and 2) comparison between different models--by evaluating the implementation process of each model and comparing the results, we can identify more mature models. The implementation of the model is evaluated from input, process, and output. Further, specific analyses and evaluations are conducted for several representative COVID-19 models to verify the validity of this evaluation framework. The results of the case study show that the proposed evaluation framework can help decision-makers identify more mature and referential models, and point out the directions where modelers can improve their models. IEEE

Interactions between different policy instruments in real markets can lead to a mismatch between stakeholders striving for individual benefits and the attainment of an overall system optimum. These distortion effects are of particular relevance for sector-integrated systems with high prosumer shares. Distributed battery storage systems, in particular, could constitute a pivotal source of flexibility in close to 100% renewable energy systems. However, depending on their operation mode, these battery systems can either serve individual profit maximization or the benefit of the system as a whole. We quantify the resulting differences by means of a fundamental linear optimization model of the electricity market, which we stringently link to a stakeholder's complete cost-of-energy analysis including relevant regulatory price components. We demonstrate that the effects encompass both system cost changes and distributional effects for certain stakeholder groups. We quantify the effects and apply an impact variation analysis to demonstrate that they could be relevant in view of the overall acceptance of the energy system transformation.

Security and privacy of data-in-transit are critical issues in Industry 4.0 which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this work, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. Additionally, we conduct a security analysis that proves that the scheme resists several attacks in the industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

In the early onset of the COVID-19 pandemic in the U.S., consumers experienced surprising shortages of essential goods such as toilet paper, yeast and flour, and some of their favorite meat cuts that appeared to be unrelated to the pandemic at first look. The “usual” explanations attributing these shortages to “demand spikes” (e.g., foreign suppliers, trade wars and tariffs, and “hoarding behavior” of panicked consumers) often failed to explain them, and in the best of cases, predicted only temporary shortages. However, shortages in these supply chains ended up being in many cases real “supply chain struggles,” with their true causes going beyond the “usual” ones, and revealing a set of deeper and “unusual causes.” Our detailed analysis of the affected supply chains identifies these overlooked failure factors and hidden causes. Our underestimated causes include demand shifts among market segments, siloed demand planning, bottlenecks in shared resources such as transportation and distribution, and supply chain structures driven by the economics of an efficiency era that failed in a risk-fraught environment. We conclude on the profound lessons learned from the pandemic crisis on supply chains, and the implied challenges to address for building resilient supply chains for the future. The resilient supply chains of the future require rethinking the relevant “systems” we plan and optimize (e.g., firm, industry, region, global factor resources, etc.). The usual answer of building firm-specific redundancy of assets and operational flexibility might be prohibitive in the level of investment required for any one firm, or their financial stakeholders, to pursue and accept.

The increase of cyberattacks by using the Internet of Things devices has exposed multiple vulnerabilities not only on devices but also on IoT infrastructures. Now, small devices can affect different networks and their services. This paper presents a review of the vulnerabilities on wireless technologies that gives connectivity to IoT, and the experiences using Software Defined Radio SDR for implementing wireless attacks to IoT technologies are assessed. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR platforms were compared, and recent methods to overcome these attacks were identified. On the IoT reference model, the perception layer was identified as the most vulnerable. Most attacks at this level occur due to hardware limitations, physical device exposure, and heterogeneity of technologies. Future cybersecurity systems based on SDR technologies have notable advantages due to their flexibility and adaptability to new communication technologies and their potential to develop advanced tools. However, the IoT’s cybersecurity challenges are so complex that merging SDR hardware with cognitive and intelligent techniques is highly advisable. Some of these techniques could include deep learning to adapt mitigation systems to rapid technological changes.

Bipolar VSC-MTDC is a promising solution to increase power transmission capacity and system reliability for large-scale renewable energy integration. However, traditional interpolar active power control methods cannot comprehensively explore the maximum transmission capability of the bipolar VSC-MTDC under abnormal operating conditions. Considering the flexibility of the bipolar VSC-MTDC that the positive-pole and negative-pole DC networks can be operated independently, a novel fault self-recovering control strategy is proposed in this paper to provide automatic, global and timely recovery solutions under various fault conditions: (a Firstly, a coordinated time-driven and event-driven security monitoring, optimization and recovering control framework is proposed to guarantee the accuracy and speed of the corresponding calculation and control, hence to meet the timeliness requirement of fault self-recovery; (b Secondly, a global optimal power allocation calculation method is proposed to automatically calculate the optimal power allocation between the positive-pole and negative-pole DC networks; (c Thirdly, an interpolar active power dispatch method is proposed to realize the fault self-recovering control strategy based on primary control strategies of bipolar VSC-MTDC. Simulation results using MATLAB and PSCAD/EMTDC show that the proposed control strategy can timely and effectively enhance the overall power transmission capability for large-scale renewable energy integration after the fault.

The dynamic line rating (DLR) is recognized as an effective method to improve the integration of renewable energies, such as wind power by enhancing the flexibility of the system. The principle of DLR is to increase the transmission capacity of a transmission line according to the forecasted weather data. However, the uncertainties of DLR and wind power caused by the forecasting errors might bring in some severe consequences to the power system, such as line overloads. In this paper, we propose a tri-level preventive dispatch model to deal with this issue. To reduce the conservativeness of the solution, we employ the Density-Based Spatial Clustering of Application with Noise (DBSCAN) method to identify and remove the extreme scenarios of DLR. And a decomposition method is proposed to solve the tri-level model.The numerical simulations on several IEEE bus systems demonstrate the effectiveness of the proposed model.

The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques.

An innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.

So far, numerous learned models have been pressed to use in microwave imaging problems. These models however, are oblivious to the imaging geometry. It has always been hard to bake the physical setup of the imaging array into the structure of the network, resulting in a data-intensive models that are not practical. This work put forward a graph formulation of the microwave imaging array. The architectures proposed is made cognizant of the physical setup, allowing it to incorporate the symmetries, resulting in a less data requirements. Graph convolution and attention mechanism is deployed to handle the cases of fully-connected graphs corresponding to multi-static arrays. The model works with a modular fashion at node level to strike a trade-off between flexibility and efficient capture of mutual information present in measured signals. Additionally, the modular working fashion endows the model with immunity to overfitting. The graph-treatment of the problem is evaluated on experimental setup in context of anomaly localization with imaging array and has shown higher performance as compared to the popular radar technique. The thin model was realized with a feasibly procured reasonably-sized dataset in the order of few hundreds, thus eliminating the need to resort to simulations for augmentation.

This paper explores national strategies for emerging countries with ambitious policy targets for future offshore wind development. A scenario-based economic cost-benefit analysis of multiple approaches to the development of offshore wind projects is conducted based on (1) who takes the initiative on financing (public sector vs. private sector), (2) whether wind turbines are supplied locally or not, and (3) whether investment plans provide managerial flexibility to respond to changing circumstances or not (real options strategy vs. traditional investment decision-making). The analysis demonstrates that a real options strategy that allows flexibility regarding future uncertainties can significantly increase the economic value of a project regardless of the type of developer or dependence on foreign wind turbine technologies. The study concludes with a recommendation that late-mover countries focus on the combination of public-sector initiatives, a real options strategy, and the purchase of domestic turbines.

It is common that airlines encounter a disruption of a flight schedule, which is mainly caused by resource shortages. In case of a disruption, subject to scarce resources, most of airlines lose flexibility of performing aircraft recovery on the basis of business interest priorities and need to delay, swap, and cancel flights. This paper proposes an air-rail inter-modal strategy to incorporate a High-Speed Rail (HSR) transport mode into an aviation network for aircraft recovery. The air-rail inter-modal strategy focuses on occasionally operational integration of existing airside and HSR infrastructure capacities. It is different from air-rail cooperation implemented in Europe which emphasizes a long-term strategy. In addition to modelling the air-rail inter-modal strategy, an inter-modal time-band network is presented. Modelling is applied to a pure aviation network and the inter-modal network. Comparison results show that the inter-modal air-rail strategy helps to reduce the number of cancelled flights and the total disruption cost.

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a source-aware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method.

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.

Using distributed hydrological models to evaluate the effectiveness of reducing non-point source pollution by applying best management practices (BMPs) is an important support to decision making for watershed management. However, complex interfaces and time-consuming simulations of the models have largely hindered the applications of these models. We designed and developed a prototype web-based decision support system for watershed management (DSS-WMRJ), which is user friendly and supports quasi-real-time decision making. DSS-WMRJ is based on integrating an open-source Web-based Geographical Information Systems (Web GIS) tool (Geoserver), a modeling component (SWAT, Soil and Water Assessment Tool), a cloud computing platform (Hadoop) and other open source components and libraries. In addition, a private cloud is used in an innovative manner to parallelize model simulations, which are time consuming and computationally costly. Then, the prototype DSS-WMRJ was tested with a case study. Successful implementation and testing of the prototype DSS-WMRJ lay a good foundation to develop DSS-WMRJ into a fully-fledged tool for watershed management. DSS-WMRJ can be easily customized for use in other watersheds and is valuable for constructing other environmental decision support systems, because of its performance, flexibility, scalability and economy.

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods., Comment: Author copy of the manuscript submitted to IEEE Trans on Power Systems

Robustness enhancement and feature selection are the two crucial issues to be resolved in robust palmprint recognition. However, existing regression-based methods are insufficient to handle outliers and select significant features. From a statistical viewpoint, we present a general framework to intrinsically resolve the two issues. By investigating the role of outliers in the formation of coding errors, we devise a double-Laplacian mixture-error model to faithfully fit the error distribution. Additionally, we design a supervised group-sparse regularizer to enforce the locality and group sparsity of the codes. Integrating the two parts into the framework produces a nonconvex constrained problem, for which we develop an iteratively reweighted l1-l1 minimization algorithm by combining the majorization-minimization strategy and the alternating direction method of multipliers. The weighted vector learned from the error model and the local group sparsity enforced by the regularizer enable our method to better handle outliers and select more significant features than the state-of-the-art methods. Extensive experimental results verify the flexibility and robustness of our method to various contaminations.

The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the worlds largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions.

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, and the adoption of the Space-Air-Ground Integrated Network (SAGIN) architecture in vehicular networks can greatly improve the performance of vehicular networks by leveraging the respective advantages of the space, air, and ground segments on coverage, flexibility, reliability, and availability, which results in Space-Air-Ground Integrated Vehicular Networks (SAGIVNs). Trust management is an important tool for constructing trustworthy SAGIVNs, and privacy preservation is also a primary concern in SAGIVNs. They have conflicting requirements and a satisfactory balance between them is urgently required. In this article, we propose a novel Privacy-Preserving Trust Management (PPTM) scheme for the emergency message dissemination in SAGIVNs. The proposed scheme can realize precise trust management and strong conditional privacy preservation simultaneously with low communication overhead, and can provide strong applicability, strong robustness, and multiple other attractive features. Furthermore, the exhaustive theoretical analysis and simulation evaluation are detailed. The results reveal that the proposed scheme is significantly superior to the existing schemes in several aspects.

Batteries can provide valuable operational flexibility to facilitate the system efficiency. However, there lacks market environments to effectively harness and monetize the value of these assets. Most existing works apply a profit-oriented single-signal pricing scheme, which mixes the value of energy and flexibility together and may ultimately raise the electricity bill. Therefore, this paper proposes a profit-neutral double-signal retail pricing scheme that distinguishes elastic market players (i.e., batteries) from inelastic market players (i.e., inflexible loads) and quantifies the value of energy and flexibility separately. Experimental results indicate that under the incentive provided by the proposed retail pricing scheme: 1) The system efficiency benefit aligns with benefits to both batteries and inflexible loads; 2) Value of operational flexibility, contributing to improve the energy efficiency, can be transparently priced and fairly allocated among batteries; and 3) The dominant role in which inflexible loads play on determining the market price is avoided.

Despite the advantages of scalability and flexibility, Security Function Virtualization (SFV) raises concerns about its own security. To enhance the security of SFV, a promising approach is to run critical components of off-the-shelf security software inside SGX enclaves. This idea, however, is hardly practical due to the difficulty of detaching components from the monolithic security function and the unacceptable cost of running them in enclaves. In this work, we propose S-Blocks, an architecture to modularize a virtual security function (VSF) and protect its key modules with SGX in an efficient manner. Through systematically decomposing modules of a VSF into related elements, it is easy to put the key modules and elements of the VSF into an enclave. Furthermore, aiming at addressing state consistency and secure migration issues of security function scaling, we design a fine-grained state synchronization and migration mechanism to ensure lose-free, order-preserving, and state security for VSFs. To demonstrate the effectiveness of our approach, we prototype S-Blocks using Fast-Click on a real Skylake platform and implement three main types of virtual security functions based on the S-Blocks architecture. Our evaluation results show that S-Blocks only imposes a manageable performance overhead, and low latency and resource consumption when protecting VSFs.

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

The optimal dispatch of electric vehicles (EVs) aims to minimize the system operation cost while satisfying the requirements for peak shaving, congestion management and voltage regulation. However, the stochastic mobility of EVs makes dispatch difficult and requires modeling the spatial and temporal distribution of EV availability. In this paper, a trip-chain-based EV resource aggregation model considering EV flexibility similarity is developed. Then, a bilevel optimization model is formulated to enable participation of the EV aggregators (EAGGs) in the day-ahead dispatch while ensuring various system operation constraints. Finally, the proposed bilevel model is transformed into a single-level convex optimization problem that can be conveniently solved by off-the-shelf software. Simulation tests substantiate that the proposed approach is superior to the existing method in terms of higher computational efficiency and lower system operation cost.

High penetration of renewable energy such as wind power and photovoltaic (PV) requires large amounts of flexibility to balance their inherent variability. Making an accurate prediction of the future power system imbalance is an efficient approach to reduce these balancing costs. However, the imbalance is affected not only by renewables but also by complex market dynamics and technology constraints, for which the dependence structure is unknown. Therefore, this paper introduces a new architecture of sequence-to-sequence recurrent neural networks to efficiently process time-based information in an interpretable fashion. To that end, the selection of relevant variables is internalized into the model, which provides insights on the relative importance of individual inputs, while bypassing the cumbersome need for data-preprocessing. Then, the model is further enriched with an attention mechanism that is tailored to focus on the relevant contextual information, which is useful to better understand the underlying dynamics such as seasonal patterns. Outcomes show that adding modules to generate explainable forecasts makes the model more efficient and robust, thus leading to enhanced performance.

Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this paper, we propose L1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline., 8 Pages, 6 figures, Accepted RAL 2021

Due to the huge gap between the high dynamic range of natural scenes and the limited (low) range of consumer-grade cameras, a single-shot image can hardly record all the information of a scene. Multi-exposure image fusion (MEF) has been an effective way to solve this problem by integrating multiple shots with different exposures, which is in nature an enhancement problem. During fusion, two perceptual factors including the informativeness and the visual realism should be concerned simultaneously. To achieve the goal, this paper presents a deep perceptual enhancement network for MEF, termed as DPE-MEF. Specifically, the proposed DPE-MEF contains two modules, one of which responds to gather content details from inputs while the other takes care of color mapping/correction for final results. Both extensive experimental results and ablation studies are conducted to show the efficacy of our design, and demonstrate its superiority over other state-of-the-art alternatives both quantitatively and qualitatively. We also verify the flexibility of the proposed strategy on improving the exposure quality of single images. Moreover, our DPE-MEF can fuse 720p images in more than 60 pairs per second on an Nvidia 2080Ti GPU, making it attractive for practical use. Our code is available at https://github.com/dongdong4fei/DPE-MEF .

The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber-physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling, i.e., the network architecture must serve a plethora of application scenarios with differing service-level agreement (SLA) requirements over the same physical infrastructure in an end-to-end manner. At the same time, since the different use cases might belong to a variety of different stakeholders, IoST must support multi-tenancy and functional isolation of services. Consequently, a network slicing framework is vital to the success of IoST. To this end, an automatic network slicing framework for space-ground integrated networks is presented in this paper. The proposed framework has been designed to address the dual objectives of route computation and resource allocation with minimal SLA violations. Different from the existing state-of-the-art, the framework presented herein is purpose-built for ultra-dense CubeSat networks, and is fully automated. In other words, the framework is purely SLA-based, and does not require prior information concerning the resource requirements associated with a slice. Other key innovations introduced through this framework include a robust SLA model for slice customization, a novel topology construction mechanism, and a unique segment routing-based online admission control solution. Furthermore, the flexibility and efficacy of the proposed framework have been evaluated through a comprehensive use case driven evaluation scenario.

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

Conventional Semi-supervised learning (SSL) approaches are limited by the reliance on assumptions of samples (manifold assumption, cluster assumption and smoothness assumption) and may fail to reach their full potential when it comes to industrial scenarios with complicated data characteristics. In this paper, a semi-supervised process quality evaluation framework is proposed based on conditional variational auto-encoder. Label inference process is utilized to handle unlabeled samples during training stage and to directly predict the label during the evaluation stage. Real NVP (real-valued non-volume preserving) is introduced to optimize the posterior distribution in order to increase flexibility. The proposed framework does not rely on assumptions of data distributions and it learns the hidden distributions from samples. Gear hobbing simulation and experiment investigations are conducted to verify the effectiveness of the proposed framework. The results indicate that the proposed method can achieve remarkable classification performance compared with other state-of-the-art approaches.