Distinctive Capability Research Articles

A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing.

In this work, we present a diagnosis system for rolling bearings that leverages simultaneous measurements of vibrations and machine rotation speed. Our approach combines the robustness of simple time domain methods for fault detection with the potential of machine learning techniques for fault location. This research is based on a neural network classifier, which exploits a simple and novel preprocessing algorithm specifically designed for minimizing the dependency of the classifier performance on the machine working conditions, on the bearing model and on the acquisition system set-up. The overall diagnosis system is based on light algorithms with reduced complexity and hardware resource demand and is designed to be deployed in embedded electronics. The fault diagnosis system was trained using emulated data, exploiting an ad-hoc test bench thus avoiding the problem of generating enough data, achieving an overall classifier accuracy larger than 98%. Its noteworthy ability to generalize was proven by using data emulating different working conditions and acquisition set-ups and noise levels, obtaining in all the cases accuracies greater than 97%, thereby proving in this way that the proposed system can be applied in a wide spectrum of different applications. Finally, real data from an on-line database containing vibration signals obtained in a completely different scenario are used to demonstrate the distinctive capability of the proposed system to generalize.

Mixed Quantum-Classical Liouville Equation Treatment of Electronic Spectroscopy of Condensed Systems: Harmonic and Anharmonic Electron-Phonon Coupling.

This Review integrates the use of electronic optical response function theory and the mixed quantum-classical (MQC) Liouville equation (MQCLE), thereby leading to electronic spectroscopy in MQC media. It further sheds light on the applicability, utility, and efficiency of the mixed quantum-classical dynamics (MQCD) formalism, which starts off with the MQCLE, in probing spectroscopy and dynamics of condensed systems, whereby quantum mechanics and classical mechanics are combined systematically. The author has been exploring and implementing MQCD to investigate electron-phonon coupling effects on electronic dephasing in harmonic and anharmonic systems by calculating linear and nonlinear optical transition analytically and numerically dipole moment time correlation functions in an MQC environment, thereby presenting an in depth spectral profile analysis and their shape and symmetry. The distinctive capability of the MQC time correlation functions is that ergodicity and stationarity properties are inherently satisfied as part of the mixed quantum-classical dynamics (MQCD) framework, unlike classical correlation functions. While some research groups have applied MQCLE to calculate vibrational spectra to study hydrogen-bonded complexes in a MQC environment and other groups calculated Optical response function to probe electron transfer dynamics using the basis mapping technique, the approach, purpose, rigor, applications, and path to the end results reported herein are different. Finally, the same framework is employed to study dissipative systems in the MQC limit, whereby the zero-phonon line adopts the correct width and eliminates its asymmetry. While the full quantum mechanical model, like the multimode Brownian oscillator (MBO) model, yields the correct width and inaccurate shape in the low-temperature limit, the MQCD formalism seems to produce an accurate zero-phonon profile. Nonlinear optical signals are also reviewed in MQC media to show the applicability and utility of this approach. The vibronic optical response functions developed here will account for geometry change, frequency change, and anharmonicity upon electronic excitation to accurately probe electronic dephasing, electron-phonon coupling, shape, and symmetry of profiles and present differences and similarities to the MBO model on pure electronic dephasing. Frequency change and anharmonicity are vitally crucial for accurately assessing electron-phonon coupling upon electronic excitation. This is an additional unique result obtained by the author to further demonstrate the applicability and utility of this approach over other approximation schemes in probing electronic dephasing including that of the MBO model.

Graphite–hexagonal diamond hybrid with diverse properties

The recently discovered graphite–diamond hybrid materials (Gradia) with mixed sp2- and sp3-hybridizations have opened up a new direction in carbon allotropes research. Herein, we reported Gradia-HZ, constituted by interfaced graphite and hexagonal diamond parts in the unit cell, which demonstrates distinct electronic and mechanical properties. With the modulation of graphite width, Gradia-HZ exhibits unexpected topological nodal-line semimetal, semiconductor, and normal metal integrating with a distinctive Quasi-1D electronic transport capability based on first-principles calculations. More interestingly, pressure-induced graphite phase transformation might be an implementable and effective method to regulate the structure and physical properties of Gradia-HZ. The discovery of rich and peculiar physical properties in Gradia-HZ, e.g., high-conductivity metals, semiconductors with variable bandgap, and topological semimetals, will arouse great research interest to graphite–diamond hybrid materials, to promote their development and application in advanced devices.

Why supply chain complexity prevails: mapping the complexity capability development process

PurposeComplexity has been called the 21st-century supply chain (SC) challenge. Most SC managers view it as a necessary evil, ever-present, costly and tough to manage, and few prioritize it. Still, anecdotes suggest some leverage it to drive operational excellence. This study aims to explore how they do it, delving into the development of a complexity management capability, under what circumstances it emerges and its effect on competitiveness.Design/methodology/approachTo better understand why, and how, companies develop (or not) a distinctive SC complexity management capability, this study employed an inductive study of 10 leading European companies, each operating a complex SC.FindingsAlthough SC complexity raises costs, increases disruptions and makes decision-making difficult, few companies have made complexity management a priority. Among those, most focus on reducing or absorbing complexity to improve operational excellence. A few invest to develop a distinctive SC complexity management capability. They manage complexity for market success. The interaction among competitive pressures, managerial attitudes and investments delineate a dynamic capability development process.Research limitations/implicationsDespite extensive research on complexity drivers, the tools used to manage SC complexity and the impact of SC complexity on performance, the interplay among factors that promote, or hinder, the development of an SC complexity capability continues to be poorly understood. By mapping the complexity capability development process, this study explicates a more nuanced approach to managing SC complexity that can yield a competitive edge.Practical implicationsSC complexity prevails because the dynamic, iterative complexity capability development process is overlooked. Managers can use the complexity capability roadmap to assess the cost/benefits of pursuing a distinctive complexity management capability more accurately.Originality/valueThis study demystifies the development of a complexity management capability, showing how some companies develop the capability to distinguish between value-added and value-dissipating complexity and thus become empowered to leverage SC complexity for competitive advantage.

Polymeric Fe3O4 Nanoparticle/Carbon Nanofiber Hybrid Nanocomposite Coatings for Improved Terahertz Shielding

With the rapid advancement in THz technology and continuously increasing number of electrical and electronic devices in this frequency band, highly efficient and functional THz shielding materials are required to ensure the intended working of these devices. Polymeric composites of hybrid nano-fillers including CNFs and magnetic nanoparticles possess distinctive capability of providing high shielding efficiency primarily absorptive in nature with field functional properties. In this article, lightweight polymeric nanocomposite coatings of continuous carbon nanofibers and magnetite (Fe3O4), as potential shielding material for the THz frequency band, have been prepared using a cost-effective conventional air-spray coating method. The obtained results are quite exclusive, as the sample with 40 wt % Fe3O4 outperforms at a very low thickness value of 540 μm, showing the highest SE value of ∼60 dB. These results are superior compared to the results obtained from most of the contemporary polymer composite materials, which usually provide good shielding at high thickness values in the mm range. Also, this study establishes that shielding performance of the polymeric hybrid nanocomposites (Fe3O4 + CNFs) is substantially better than a single nanocomposite CNF filler previously synthesized and characterized through the same approach.

A distance Protection/Fault location algorithm for Double-Circuit transmission lines with asymmetrical circuits during Cross-Country faults

The impedance-measuring elements and fault locators in distance relays suffer from measuring the correct fault impedance and fault location when facing cross-country faults (CCFs). During such atypical faults, this paper proposes a distance protection along with a fault location algorithm for double-circuit lines with asymmetrical circuits. This is fulfilled through instantaneous fault-branch currents innovatively derived from the line six-phase circuit model analysis on a time-domain basis. The currents derived lead to the relations that are mathematically linear combinations of designated vectors. This makes the possibility of establishing vector-input adaptive linear combiners (VI-ALC) in accordance with the relations derived. Being fed from the vectors of instantaneous measurements at the relaying side, the VI-ALCs with a distinctive capability of online training would swiftly give an accurate estimation of the fault distances during CCFs. The estimations are exploited in a logical procedure for both of the distance protection and fault location functions, taking account of different position status of the CCFs’ comprising LG faults. The proposed algorithm is feasibly implementable and is independent of the system configuration. It operates robustly in high-ground resistance and noisy conditions. Through extensive simulation studies in PSCAD/EMTDC, performance of the proposed algorithm would be investigated and verified in a variety of fault scenarios.

Advancing supply chain management from agility to hyperagility: a dynamic capability view.

Research and practice emphasize the criticality of supply chain agility in responding to external disruptions. However, many organizations struggled to respond at enhanced speed to the global supply chain shocks caused by the COVID-19 pandemic. Organizations need hyperagile supply chains to survive and remain competitive in an environment characterized by unexpected and sudden disruptions such as the COVID-19 pandemic. We propose that supply chain hyperagility (SCH) is a distinctive organization-specific capability. It enables organizations to effectively manage demand shocks at extreme speeds and under extreme time pressures. We advance the concept of supply chain hyperagility and establish its antecedents, taking the dynamic capability perspective. This study operationalizes the SCH construct for the first time and investigates its antecedents using structural equation modeling. The results highlight the significance of data analytical capabilities, market orientation, entrepreneurial orientation, and supply chain integration in shaping supply chain hyperagility. The study offers practical insights for managers regarding designing supply chains that can navigate hyperagile environments and benefit from the opportunities presented by such environments.

Pyrometallurgical processing of deactivated nickel catalysts on Al2O3 carrier

The paper considers the use of deactivated nickel-containing catalysts based on Al2O3 as a significant raw material resource of one of the most important metals. The research highlights the features of this secondary nickel source that determine the acceptable methods of processing such raw materials. The effect of fluxing additives on the properties of the melt containing catalysts prepared beforehand has been studied subject to limitations as to their list (lime, fluorspar) in order to implement a pyrometallurgical method of metal extraction featuring by a relatively small amount of additives themselves. Due to induction heating used in combination with a graphite crucible, adding the total amount of fluorspar and marble additives close in mass made it possible to obtain the melt at a temperature slightly higher than the nickel melting temperature. In this case, the level of metal losses was about 2 %, which indicates the applicability of this method in laboratory conditions to ensure correct incoming analysis. It was proposed to use the pyrometallurgical method on an industrial scale using closed arc heating. To confirm this conclusion, experiments were conducted with a representative (more than 100 kg) amount of catalyst using a tailored arc furnace. Graphite chips were used as a reducing agent. The necessity of electrical matching of the load with the power source resulted in some adjustment of the fluxing additive ratio towards a reduction of calcium oxide content. As a result of a series of experiments, nickel with an up to 5 % iron admixture, similar in composition to the metal formed in the graphite crucible, was obtained. The presence of iron was caused by the fundamentally distinctive capability of the pyrometallurgical technology to reduce unstable compounds. Therefore it was suggested to use this metal for ferronickel production. The use of scarce fluorspar is justified by the fact that the resulting slag can be in demand in the production of fluxes for the electroslag remelting process.

Constructing interstitial pillar to manipulating interlamination interaction force: Towards high sodium-content P2/O3 intergrowth cathodes

As promising sufficient sodium-ion reservoirs, layered O3-type binary nickel manganese oxides always suffer from terrible structural instability originating from the extraction and insertion of Na atoms. Constructing P2/O3 biphasic composites have been proved to be an effective way for enhancing the cyclability of layered sodium oxide cathodes, which are usually realized through inactive cations substitution and sodium content regulation. Herein, a novel strategy was developed to construct high concentration P2/O3 intergrowth layered cathodes via manipulating the interlayer interaction. Guided by the DFT calculations, the interval [BO4]3+ electriferous groups are beneficial to modulating the interlayer configuration, enhancing the oxygen stability and structural stability of layered oxide cathode. The high sodium-content P2/O3 composite exhibits a superior reversible capacity (159.1 mAh g−1 at 0.2 C, 1 C = 200 mA g−1), distinctive rate capability (75.3 mAh g−1 at 5 C) and excellent cycling stability with a capacity retention of 74.1% after 300 cycles at 1 C within wide potential range of 2.0–4.2 V. The results highlight that the synergetic effect of sufficient-sodium P2/O3 hybrid structure induced by manipulating the interlayer interaction is an effective strategy for achieving electrochemically stable layered cathode material for sodium-ion batteries.

Controlled biomolecules separation by CO2-responsive block copolymer membranes

The intelligent responsive membranes have aroused much attention due to their distinctive capability to reversibly change separation performances under the stimulation of ambient environment. Especially, the responsive membranes derived from block copolymers, which have regular nanoporous structures, display great potential in high-precision controllable separation. Herein, we use CO2 gas as the non-toxic, mild stimulus, and develop the responsive membranes from a block copolymer of poly(2-diethylaminoethyl methacrylate)-block-polystyrene (PDEAEMA-b-PS) by the selective swelling method. The membranes exhibit a bi-continuous nanoporous structure and the surfaces and pore walls are rich with CO2-responsive PDEAEMA chains. Based on the reversible conformation transition of PDEAEMA chains between the collapsed state and extended state upon CO2/N2 stimulation, the membranes achieve the controllable regulation on the water permeances from ∼100 to ∼2100 L⋅h−1⋅m−2⋅bar−1, also realize the blocking/passing switch for varied proteins. More importantly, the extended PDEAEMA chains shrink the effective pore size down to less than 5 nm, moving the separation scope from ultrafiltration to tight-ultrafiltration. Thus, the membranes are capable to separate macromolecular proteins with small molecule polypeptide and vitamin with high separation efficiencies, demonstrating their application prospect in precise separation and fractionation of biomolecules.

Light‐Induced Cold Marangoni Flow for Microswarm Actuation: From Intelligent Behaviors to Collective Drug Delivery

AbstractMicroswarms have shown great potential in biomedical applications at small scales due to their features of wireless actuation and collective microrobotic behaviors. However, actuation of microswarms with strong universality and intelligent biomimetic behaviors is always a great challenge. Here, photothermal damage‐free actuation of an intelligent microswarm based on light‐induced cold Marangoni flow (CMF) is reported, along with collective drug delivery and targeted cell chemotherapy. This microswarm actuation shows strong universality and high controllability for both abiotic particles and living materials. Importantly, this microswarm exhibits intelligent biomimetic behaviors, such as collective migration, size‐based self‐organization, and group rejection, due to the synergy between cold flow with individual agents. The distinctive photothermal isolation capability of CMF ensures the microswarm to perform photothermal damage‐free biomedical tasks such as collective gene delivery and targeted single‐cell chemotherapy. This light‐induced CMF provides an optical strategy for photothermal damage‐free actuation of intelligent biomimetic microswarm robots, with great promises to perform many collective and cognitive tasks in biomedical applications such as cooperative grasping, collective drug delivery, and precise chemotherapy.

A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes.

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of <0.0001, enabling risk stratification. Risk stratification is validated by a. our random forest model that efficiently classifies (AUC = 97%) patients into two risk groups (low- vs. high-risk) by learning their 3-miRNA expression values, and b. our deep learning scheme, which recognizes those patterns that differentiate the images in question. Molecular-clinical correlations were drawn to classify low- (OS > 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

Anti-Overturning Fully Symmetrical Triboelectric Nanogenerator Based on an Elliptic Cylindrical Structure for All-Weather Blue Energy Harvesting

HighlightsThe novel elliptical cylindrical structure (EC) provided an excellent self-stability, high sensitivity in small agitations, and most importantly, a distinctive anti-overturning capability for the EC-triboelectric nanogenerator (EC-TENG).Benefitting from the fully symmetrical design, the EC-TENG could maintain the original output after being overturned under extreme conditions, distinguishing itself from previous TENGs.Two built-in TENGs designed for use in rough seas and tranquil seas improved the collection efficiency for all-weather wave energy.

Directional and Adaptive Oil Self‐Transport on a Multi‐Bioinspired Grooved Conical Spine

AbstractManipulating oil droplets in an aqueous solution offers great opportunities to develop advanced devices in the fields of water remediation, chemical micro‐reactor, etc. Although conical structures can achieve a directional oil droplet motion, the continuous and adaptive fluid self‐transport in complicated environments is still a challenge. Inspired by the distinctive oil transport capability of fishbone and the anisotropic grooved structure of rice leaves, this work presents a multi‐bioinspired grooved conical spine (BGCS) for improved oil manipulation. Benefiting from the cooperative effect of the asymmetric Laplace pressure and the surface capillary force, the BGCS possesses the reliable functions of ultrafast and continuous oil transport under water, in air, and even across the air–water interface. Compared with the original cone, the BGCS demonstrates a nine times faster transporting velocity and a five times larger capacity. It is envisioned that this updated oil‐collecting cone with its cooperative structure should find real‐world applications.

Ultrasmall iron oxide nanoparticles cause significant toxicity by specifically inducing acute oxidative stress to multiple organs

BackgroundIron oxide nanoparticles have been approved by food and drug administration for clinical application as magnetic resonance imaging (MRI) and are considered to be a biocompatible material. Large iron oxide nanoparticles are usually used as transversal (T2) contrast agents to exhibit dark contrast in MRI. In contrast, ultrasmall iron oxide nanoparticles (USPIONs) (several nanometers) showed remarkable advantage in longitudinal (T1)-weighted MRI due to the brighten effect. The study of the toxicity mainly focuses on particles with size of tens to hundreds of nanometers, while little is known about the toxicity of USPIONs.ResultsWe fabricated Fe3O4 nanoparticles with diameters of 2.3, 4.2, and 9.3 nm and evaluated their toxicity in mice by intravenous injection. The results indicate that ultrasmall iron oxide nanoparticles with small size (2.3 and 4.2 nm) were highly toxic and were lethal at a dosage of 100 mg/kg. In contrast, no obvious toxicity was observed for iron oxide nanoparticles with size of 9.3 nm. The toxicity of small nanoparticles (2.3 and 4.2 nm) could be reduced when the total dose was split into 4 doses with each interval for 5 min. To study the toxicology, we synthesized different-sized SiO2 and gold nanoparticles. No significant toxicity was observed for ultrasmall SiO2 and gold nanoparticles in the mice. Hence, the toxicity of the ultrasmall Fe3O4 nanoparticles should be attributed to both the iron element and size. In the in vitro experiments, all the ultrasmall nanoparticles (< 5 nm) of Fe3O4, SiO2, and gold induced the generation of the reactive oxygen species (ROS) efficiently, while no obvious ROS was observed in larger nanoparticles groups. However, the ·OH was only detected in Fe3O4 group instead of SiO2 and gold groups. After intravenous injection, significantly elevated ·OH level was observed in heart, serum, and multiple organs. Among these organs, heart showed highest ·OH level due to the high distribution of ultrasmall Fe3O4 nanoparticles, leading to the acute cardiac failure and death.ConclusionUltrasmall Fe3O4 nanoparticles (2.3 and 4.2 nm) showed high toxicity in vivo due to the distinctive capability in inducing the generation of ·OH in multiple organs, especially in heart. The toxicity was related to both the iron element and size. These findings provide novel insight into the toxicology of ultrasmall Fe3O4 nanoparticles, and also highlight the need of comprehensive evaluation for their clinic application.Graphical

A Combinatorial RGB and Depth Images CNN-based Model for Oil Palm Fruit Bunch Detection and Heatmap Localisation for a Visual SLAM System

The harvesting job of cutting and collecting fruit bunches in oil palm plantations remains the most labour-intensive job in the oil palm processing cycle. The introduction of an autonomous vehicle to assist workers in the harvesting job promises better productivity. Such a driverless vehicle requires a software module known as simultaneous localisation and mapping (SLAM) to guide the vehicle to navigate autonomously. This work proposes a visual SLAM system with a distinctive capability of detecting and localising oil palm loose fresh fruit bunches (FFB) on the ground using intelligent image processing. This vehicle is equipped with a depth camera capable of capturing RGB images and depth images concurrently. Two VGG16-based convolutional neural network (CNN) models are trained using the acquired RGB and depth images dataset of loose FFBs on the ground. The output from the combinatorial FFB detection model is then fed into a visual SLAM system called RTAB-Map. By combining the FFB detection model and the visual SLAM system, the vehicle can plan for autonomous navigation safely, perform bunch pick-up tasks, and avoid collision with fruit bunches on the ground. The experiment results show that the proposed CNN model can detect and localise loose FFBs with significant accuracy in various lighting conditions.

Identify the Activity Origin of Pt Single-Atom Catalyst via Atom-by-Atom Counting.

Atom dispersion in metal supported catalysts is vital as it structurally accounts for their catalytic performances. Since practical catalysts normally present structural diversity, such as the coexistence of single atoms, clusters, and particles, traditional spectroscopy methods including chemisorption, titration, and X-ray absorption, however, provide only an averaged description about the atom dispersion but are not able to distinguish localized structural divergence. In this work, through developing a methodology of electron-microscopy-based atom recognition statistics (EMARS), catalyst dispersion has been redefined at atomic precision in real space via the statistically counting 18 000+ Pt atoms for a Pt/Al2O3 industrial reforming catalyst. The EMARS results combined with in situ microscopy evidence disclose that the activity for aromatics production quantitatively correlates with the density of Pt single-atoms, while Pt clusters contribute no direct activity but could kinetically transform into single-atoms when being heated under an oxidative atmosphere. Compared to EMARS, the traditional hydrogen-oxygen titration method is found to induce serious bias in the Pt dispersion in reference to actual activity. This distinctive capability of EMARS for metal dispersion quantification offers a possibility of directly identifying the catalysis roles of different metal species in a practical catalyst via atom-resolved statistics.

Philosophy for children and logic-based therapy

This article aims to shed light on the interconnectedness between two important projects in applied philosophy: (a) Philosophy for Children (P4C), a movement for the introduction of philosophy in schools, and (b) Logic-based Therapy and Consultation (LBTC), a widely developed form of philosophical counselling. More specifically, it attempts to show how Michael Hand’s (2018) argument in favour of P4C can fruitfully be enhanced by the endorsement of fundamental theoretical assumptions of Elliot Cohen’s (2005, 2019) LBTC. Hand argues that philosophy should be taught in schools as a mandatory subject by virtue of its distinctive educational value, namely its ability to contribute to justifying subscription to moral, political and religious standards. In turn, proponents of LBTC presuppose the distinctive capability of philosophy to assess and direct subscription to emotional standards, as well as to a broader category of standards, the practical ones. Given that subscribing to all those types of standards is of utmost importance for human life, an argument for mandatorily introducing philosophy in the school curricula starts taking a more concrete shape.

The conceptual framework on integrated flexibility: an evolution to data-driven supply chain management

PurposeThis paper aims to establish a relationship between supply chain integration (SCI) and supply chain flexibility (SCF) to develop a two-dimensional approach, i.e. integrated flexibility.Design/methodology/approachBuilding on a relational view and dynamic capability theory, this paper argues that integrated flexibility is the strategy that enables organisations to achieve different positions and states to create distinctive capability. The article has proposed the conceptual framework that connects different supply chain strategies and practices to improve supply chain performance (SCP) considering the cross-disciplinary approach.FindingsThe conceptual framework around the new perspective, i.e. integrated flexibility, is built to deal with issues related to operations management. The paper suggests examining the mediating effect of SCF between SCI and SCP and the moderating role of knowledge management (KM), data analytics (DA) and quality management (QM) practices on their relationship. Moreover, research direction in terms of propositions and implications are developed to showcase how underlying practices streamline the supply chain and lead to superior SCP.Practical implicationsThe proposed framework discusses the degree of integration and flexibility levels to guide practitioners in designing a supply chain strategy with their partners and answering how much resources need to be extended to achieve flexible operations and realise SCP.Originality/valueAuthors have developed an entirely new integrated flexibility concept that provides a base to sustain in the competitive market. The foundation of integrated flexibility is built on relational view and dynamic capability theory and supported by DA, QM and KM.

LeGO: A Learning-Guided Obfuscation Framework for Hardware IP Protection

The security of hardware intellectual properties (IPs) has become a significant concern, as the opportunity for piracy, reverse engineering, and malicious modification is increasing. Hardware obfuscation has been studied as a potent method to protect against all these attack vectors. However, most of the existing obfuscation techniques have been successfully compromised, where many inherent functional or structural vulnerabilities in these techniques are utilized to reveal the obfuscation key or retrieve the original design. In this article, we introduce LeGO, a learning-guided obfuscation framework that overcomes known vulnerabilities in a scalable and systematic manner, leading to a robust and lightweight locking mechanism. The proposed framework is guided by our security evaluation process that performs a thorough assessment of an obfuscated IP against various attacks and identifies the vulnerabilities. It then judiciously selects and applies a set of design modification steps or rules that can eliminate these vulnerabilities. Such a rule-based obfuscation process has the distinctive capability to address all existing as well as emerging attacks through the learning of appropriate design transformation steps that prevent these attacks. We present an efficient strategy to apply these rules on a design, while resolving any conflict. Our evaluation of the LeGO framework on a set of ISCAS85 and open-source IP benchmarks has shown promising results in terms of robustness against diverse attacks with an average of area, power, and delay overhead of 39%, 45%, and 15%, respectively.