Enhancement Scheme Research Articles

A hybrid lightweight blockchain based encryption scheme for security enhancement in cloud computing

A hybrid lightweight blockchain based encryption scheme for security enhancement in cloud computing

Modeling of nonlinear and nonstationary stochasticity for atomic ensembles

This paper addresses the problem of stochastic modeling of atomic ensembles under multi-source noise and makes the model interpretable. First, based on Itô’s lemma and Allan variance analysis (ITÔ-AVAR), an approach is proposed to model nonstationary stochastic submodels of atomic ensembles. On this basis, the variance decomposition and nonlinear optimization algorithms are utilized to hybridize modeling atomic ensembles with nonlinear and nonstationary properties. Second, an Itô’s lemma dynamic allan variance analysis (ITÔ-DAVAR) approach is developed for online modeling of atomic ensembles. Further, an atomic ensembles sensitivity enhancement scheme based on the proposed approach is given, which effectively promotes the progress of quantum instrument engineering. Finally, the proposed scheme are deployed in the optical pumping magnetometer and spin-exchange relaxation-free comagnetometer, respectively, while experimentally verifying the sensitivity of the spin-exchange relaxation-free comagnetometer reaches 5.36×10−6degs−1Hz−1/2.

Efficient capacity enhancement using OFDM with interleaved subcarrier number modulation in bandlimited UOWC systems.

We propose and demonstrate an efficient capacity enhancement scheme for bandlimited underwater optical wireless communication (UOWC) systems by utilizing orthogonal frequency division multiplexing with interleaved subcarrier number modulation (OFDM-ISNM). In the proposed OFDM-ISNM, joint number and constellation mapping/de-mapping is utilized to avoid error propagation and subblock interleaving is further applied to address the low-pass effect of the bandlimited UOWC system. The feasibility and superiority of the proposed OFDM-ISNM scheme for practical bandlimited UOWC systems have been verified through both simulations and experiments. The obtained results demonstrate that the proposed OFDM-ISNM scheme is capable of efficiently improving the achievable data rate of the bandlimited UOWC system. Specifically, the experimental results show a significant 28.6% capacity enhancement by OFDM-ISNM over other benchmark schemes, achieving a data rate of 3.6 Gbps through a 2-m water channel.

Cascading failure model and resilience enhancement scheme of space information networks

Space information networks provide global users with high-speed Internet services. The failure of a small number of satellites can trigger cascading failures that can bring down the network. Therefore, enhancing the network’s resilience under cascading failures is crucial. In this paper, we establish a cascading failure model for space information networks for the first time. The gravity model-based load calculation method, entity failure mechanisms, and new survivability measures are presented. The load unbalances of satellites are the leading cause of cascading failures, and the layout of the gateways determines the final load distribution. Therefore, a load-oriented gateway layout optimization model is developed. In order to obtain better optimization performance, a gateway layout solution algorithm based on the cuckoo search is given. Exhaustive simulations are carried out in a case satellite constellation, and the results show that the proposed approach is reasonable and able to enhance the resilience of the network under cascading failures.

Development of Advective Dynamic Stabilization scheme for ISPH simulations of free-surface fluid flows

This study presents an enhanced version of a numerical stabilizing scheme, namely, the Dynamic Stabilization scheme (DS) by Tsuruta et al. (2013), referred to as the Advective DS scheme (ADS) for further enhancement of the numerical stability and accuracy in ISPH simulations of free-surface fluid flows. To ensure general stability of calculations, particle methods may benefit from particle regularization schemes including artificial repulsive force schemes to keep particles equally distanced. DS explicitly provides a stabilizing force based on the overlapped state of a target particle and its neighboring particle, resulting in a momentum-conservative repulsive force and stable simulations. However, DS may also result in numerical noises and numerical dissipation. Such numerical noises stem from the excess stabilizing forces because of consideration of the particle positions only. The approaching velocities of particles, corresponding to the divergence-free condition, are ignored. To precisely consider the dynamic states of particles in derivation of stabilizing forces, in ADS, the approaching velocities are additionally considered and the stabilizing forces are derived in a variationally consistent form based on time rate of particle overlapping state, to precisely recover the exact deteriorated particle regularity state. The enhanced performance of the proposed ADS scheme is shown through some benchmark tests including a Taylor-Green vortex, a dam break as a violent free-surface flow, an oscillating drop and a rotating square patch. The enhanced performance of ADS in terms of reproduced pressure and velocity fields, energy conservation and convergence are well demonstrated.

Low-Light Image Contrast Enhancement with Adaptive Noise Attenuator for Augmented Vehicle Detection

The rapid progress in deep learning technologies has accelerated the use of object detection models, but most models do not operate satisfactorily in low-light environments. As a result, many studies have been conducted on image enhancement techniques aiming to make objects more visible by increasing contrast, but the process of image enhancement may negatively impact detection as it further strengthens unwanted noises due to indirect factors of light reflection such as overall low brightness, streetlamps, and neon signboards. Therefore, in this study, we propose a technique for improving the performance of object detection in low-light environments. The proposed technique inverts a low-light image to make it similar to a hazy image and then uses a haze removal algorithm based on entropy and fidelity to increase image contrast, clarifying the boundary between the object and the background. In the next step, we used the adaptive 2D Wiener filter (A2WF) to attenuate the noise accidentally strengthened during the image enhancement process and reinforced the boundary between the object and the background to increase detection performance. The test evaluation results showed that the proposed image enhancement scheme significantly increased image perception performance with the perception-based image quality evaluator being 12.73% lower than existing image enhancement techniques. In a comparison of vehicle detection performance, the proposed technique for enhancing nighttime images combined with the detection model proved its effectiveness by increasing the average precision by up to 18.63% against existing detection methods.

Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system’s objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.

A Lightweight Block With Information Flow Enhancement for Convolutional Neural Networks

Convolutional neural networks (CNNs) have demonstrated excellent capability in various visual recognition tasks but impose an excessive computational burden. The latter problem is commonly solved by utilizing lightweight sparse networks. However, such networks have a limited receptive field in a few layers, and the majority of these networks face a severe information barrage due to their sparse structures. Spurred by these deficiencies, this work proposes a Squeeze Convolution block with Information Flow Enhancement (SCIFE), comprising a Divide-and-Squeeze Convolution and an Information Flow Enhancement scheme. The former module constructs a multi-layer structure through multiple squeeze operations to increase the receptive field and reduce computation. The latter replaces the affine transformation with the point convolution and dynamically adjusts the activation function’s threshold, enhancing information flow in both channels and layers. Moreover, we reveal that the original affine transformation may harm the network’s generalization capability. To overcome this issue, we utilize a point convolution with a zero initial mean. SCIFE can serve as a plug-and-play replacement for vanilla convolution blocks in mainstream CNNs, while extensive experimental results demonstrate that CNNs equipped with SCIFE compress benchmark structures without sacrificing performance, outperforming their competitors.

Emergency Restorability of Underground Engineering Environment after Disasters by Utilizing Prefabrication and Assembly Technology

Urban development is promoting aboveground-underground city integration. Underground engineering and surrounding buildings and infrastructure form an underground engineering environment (UEE). In critical disasters, damage to the UEE not only causes chain damage to the surroundings but also aggravates urban resilience. Few studies have focused on UEE resilience and its disaster reduction. This study investigated the emergency restorability of the UEE to fill this research gap. First, we classified the restoration levels based on UEE damage patterns. Second, we performed subregion optimization based on cast-in-situ (CIS) restoration, and technical optimization by utilizing prefabrication and assembly technology (PAT). Third, the CIS/PAT UEE emergency restorability functions were constructed based on these optimizations and the local assembly capacity. Finally, the performance of the CIS/PAT restoration, optimization effect, and local assembly capacity were analyzed using a case study. The results indicated that (1) meticulous subregion optimization significantly reduced environmental work with a 13.5% speed up; (2) technical optimization incorporating PAT had the highest efficiency for improving UEE emergency restorability; (3) PAT restoration enhanced the structural resilience and functional resilience of the UEE by at least 28.34% and 62.27% over CIS restoration, respectively; (4) upper and lower thresholds exist for assembly speed in PAT restoration; (5) there exist three types of assembly capacity in PAT restoration: ring-quantity-sensitive, ring-weight-sensitive, and insensitive; and (6) the adaptive schemes maximized the performance of different local assembly capacities in PAT restoration. The UEE emergency restorability function provides a quantitative assessment tool for the resilience of UEE and a resilience enhancement scheme for disaster reduction.

Adaptive rule-based colour component weight assignment strategy for underwater video enhancement

ABSTRACT Images and videos collected in an underwater environment often have low contrast, blur, and colour cast due to two significant sources of distortion; light scattering and absorption. In an underwater image/video, suspended particles attenuate red and blue components more than green channels. This article presents two adaptive weight allocation strategies based on rule assignment for red, green, and blue channels. Firstly, an improved balanced contrast enhancement technique (IBCET) is proposed using an adaptive contrast enhancement scheme based on colour component weight assignment. Secondly, a modified fuzzy contrast enhancement technique which obtains the intensification factor based on the weight of each component is developed. Finally, principal component analysis fusion is employed to improve the overall colour and contrast of the output video frames. Qualitative and quantitative evaluations on the standard underwater video dataset are demonstrated to validate the improved performance of the proposed algorithm.

Palmprint enhancement network (PEN) for robust identification

Despite being reliable, palmprints have not received as much attention as other biometrics such as fingerprints, face or iris. Amount of information provided by high resolution palmprints and the fact that they have huge forensic value makes them a preferred biometric choice for large scale identification systems. In palmprints, extraction of reliable features for identification is still a challenging task especially because most of palmprints found in the real world, e.g., in crime scenes, are of poor quality. This makes palmprint enhancement a crucial pre cursor to identification. Errors during enhancement result in extraction of un-reliable features which deteriorate identification accuracy. Recent works in palmprints have focused more on matching algorithms and limited novelty has been introduced in enhancement. Enhancement techniques used on high resolution palmprints recently are either borrowed from fingerprint techniques or are built on the high-risk assumption that palm ridge pattern is stationary or smooth in a local area. Large size and abruptly changing ridge pattern of palmprints dictates the need for a more robust enhancement scheme. This paper proposes a novel deep learning based high resolution palmprint enhancement approach that is able to process large areas of palmprint without making the assumption that underlying ridge pattern is stationary. We have tested proposed enhancement approach on a renowned high resolution palmprint dataset which shows that proposed technique performs favourably in comparison to state of the art.

Trusted edge and cross-domain privacy enhancement model under multi-blockchain

With its promising security and distributed qualities, blockchain offers a significant opportunity to break through the privacy protection issues in the edge computing paradigm. However, when edge participants submit security tasks to a distributed blockchain network across domains, they may expose their data privacy and location privacy. These privacy will be maliciously attacked and exploited by attackers such as external attackers and untrusted third parties, increasing the difficulty of access control. In addition, the performance bottleneck of blockchain systems cannot meet the demand for efficient data processing. To address these problems, we propose a cross-domain privacy enhancement scheme based on multi-blockchain. The scheme first integrates edge computing based on master-slave multi-blockchain and designs an identity authentication mechanism and trust assessment mechanism to deploy a three-layer trusted network architecture, which ensures the security stability and operational efficiency at the edge side. Secondly, role mapping rules are developed and trust degree is evaluated for domain nodes, and a cross-domain access control model based on trust degree and role is proposed. Data in this model cannot be exchanged across domains until all nodes jointly verify whether the access control policy is effective, which ensures the secure sharing of data in heterogeneous domains. In addition, to ensure the privacy and authenticity of data in cross-domain sharing, a hybrid searchable encryption method based on symmetric encryption and public key encryption is proposed. Finally, the security theoretical analysis proves that the model successfully ensures the non-repudiation of access control policy and the controllability of cross-domain data. Experiments show that the proposed model improves access dynamics by 74.8% and reduces CPU usage by 24.6% on average compared to traditional RBAC. The scheme is scalable, and its sending rate to throughput ratio reaches 1:1, gaining a 2X throughput advantage over existing schemes.

Optofluidic Accumulation of Silica Beads on Gel-Based Three-Dimensional SERS Substrate To Enhance Sensitivity Using Multiple Photonic Nanojets.

This paper presents a gel-based three-dimensional (3D) substrate for surface-enhanced Raman spectroscopy (SERS) mediated by photonic nanojets (PNJs) to enhance the sensitivity of SERS detection. The porous structure of the gel-based substrate allowed small molecules to diffuse into the substrate, while the placement of silica beads on the substrate surface resulted in the generation of photonic nanojets during SERS measurements. Because the gel-based SERS substrate had electromagnetic (EM) hot spots along the Z-direction for several tens of microns, the focuses of the PNJs, which were located a few microns away from the substrate surface, could excite the EM hot spots located within the substrate. Our objective was to maximize SERS signal intensity by coating the substrate with a close-packed array of silica beads to enable the generation of multiple PNJs. The bead array was formed using an optical fiber decorated with gold nanorods (AuNRs) to create a temperature gradient in a mixture containing silica beads, thereby enabling their arrangement and deposition in arbitrary locations across the substrate. In experiments, the Raman enhancement provided by multiple PNJs significantly exceeded that provided by single PNJs. The proposed PNJ-mediated SERS method reduced the limit of detection for malachite green by 100 times, compared to SERS results obtained using the same substrate without beads. The proposed enhancement scheme using a gel-based 3D SERS substrate with a close-packed array of silica beads could be utilized to achieve high-sensitivity SERS detection for a variety of molecules in a diverse range of applications.

Robust enhancement of chiller sequencing control for tolerating sensor measurement uncertainties through controlling small-scale thermal energy storage

Reliable chiller sequencing control strategy is crucial to enhancing control robustness and energy efficiency of the multiple-chiller systems. However, the commonly used conventional chiller sequencing control strategy, which determines the chiller stage by comparing the measurement instantaneous cooling load with the chiller's cooling capacity, often deviate significantly from its desired results due to the presence of measurement uncertainties. Different from the existing solutions focusing on improving the accuracy of measured cooling load using complicated models fed with large amounts of measurement information, this study presents a novel solution from a new perspective, tolerating rather than dealing with measurement uncertainties directly through introducing and controlling a small-scale thermal energy storage integrated with the chiller plant. Two schemes, namely robustness-enhancement scheme and chiller operating efficiency enhancement scheme, were developed and collaboratively utilized to achieve two objectives simultaneously: enhancing the robustness of the sequencing control and improving the operating efficiency of chillers even under measurement uncertainties. The results of case studies demonstrate that, compared with the conventional strategy, the proposed strategy can reduce the switching frequency by up to 77.42% as well as save the total energy use of chilled water systems by up to 4.44% without sacrificing indoor thermal comfort.

Wavelength-independent light extraction enhancement by nanostructures for scintillators with broadband emission

Conventional photonic crystals with a single period are capable of enhancing the light output of scintillators based on the partial elimination of internal total reflection. However, the enhancement often comes along with a strong wavelength dependence, leading to a severe spectrum distortion. Here, the nanostructures for wavelength-independent light extraction enhancement, namely, multi-size spherical nanostructures, are designed and fabricated. The nanostructure can efficiently outcouple the scintillation light and at the same time significantly weaken the wavelength dependence. The light output of the scintillators with the nanostructures obtains noticeable enhancement with a ratio of 1.7, having weak wavelength dependence, which is in contrast to the enhancement by single-periodic photonic crystals. In addition to the applications on the scintillators, we anticipate that these wavelength-independent light output enhancement schemes could be expanded to other important fields, such as white light-emitting diodes and display devices.

UC-SFDA: Source-free domain adaptation via uncertainty prediction and evidence-based contrastive learning

Most unsupervised domain adaptation approaches learn domain-invariant features assuming that source and target domain data are available simultaneously. In practice, the availability of source samples is only sometimes possible. This paper establishes a novel source-free domain adaptation (SFDA) framework based on uncertainty prediction and a neighborhood-guided evidence-based contrastive learning scheme. First, we develop an evidence analyzer based on the uncertainty prediction principle of Dempster–Shafer (D–S) evidence theory, which improves the network capability for discriminating different types of samples. The transformer layer with a self-attention module is adopted to capture long-distance feature dependencies such that the proposed network has better generalization ability on multiple domains. Then, we offer a high-confidence target domain sample (HCS) acquisition strategy through evidence theory, entropy criterion, and distance information. A joint confidence enhancement scheme obtains the final HCS that generates pseudo-labels. Finally, we propose an optimization method based on evidence theory, evidence-based comparative learning, and internal neighborhood structure to ensure the separability between classes and compactness within categories. Experimental results show that the proposed framework performs superiorly on two standard datasets on multiple adaptation tasks. The code for this project is available at github.com/oolown/UC-SFDA.

Photothermal contributions to H–Pd system

Temperature is one of the most critical parameters to control reaction process, increasing reaction temperature can promote the activity of the catalyst. Thus, a high priority will be to effectively accelerate the chemical reaction and improve the efficiency of chemical reaction by using appropriate means to raise reaction temperature. Herein, hydrogen-palladium (H–Pd) reaction, as a reversible reaction, is the internal physicochemical mechanism of Pd-catalyzed hydrogen purification and Pd-based hydrogen detection. According to temperature dependence of H–Pd reaction, it is significance to develop an H–Pd reaction temperature enhancement scheme to accelerate reaction and improve reaction efficiency, which is important to design/optimize Pd-based hydrogen sensor and understand the relationship between reaction course and temperature. Therefore, we demonstrate an all-optical H–Pd reaction temperature enhancement scheme assisted by photothermal effect. In addition, for analyzing the relation between H–Pd reaction and temperature, we designed an optics-mechanics coupling-based fiber hydrogen sensor, it can convert the influence of photothermal-induced reaction temperature raising on H–Pd reaction to its hydrogen response. These experimental results demonstrate that this proposed photothermal effect-assisted reaction temperature enhancement scheme can be used as an effective strategy to improve H–Pd reaction, and it has the great potential to achieve significant performance improvements of hydrogen detection.

The enhancement scheme for the predictive ability of QSAR: A case of mutagenicity

Mutagenicity is one of the most dangerous properties from the point of view of medicine and ecology. Experimental determination of mutagenicity remains a costly process, which makes it attractive to identify new hazardous compounds based on available experimental data through in silico methods or quantitative structure-activity relationships (QSAR). A system for constructing groups of random models is proposed for comparing various molecular features extracted from SMILES and graphs. For mutagenicity (mutagenicity values were expressed by the logarithm of the number of revertants per nanomole assayed by Salmonella typhimurium TA98-S9 microsomal preparation) models, the Morgan connectivity values are more informative than the comparison of quality for different rings in molecules. The resulting models were tested with the previously proposed model self-consistency system. The average determination coefficient for the validation set is 0.8737 ± 0.0312.

Hybrid FSK–FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication

In this paper, we present a hybrid frequency shift keying and frequency division multiplexing (i.e., FSK–FDM) approach for information embedding in dual-function radar and communication (DFRC) design to achieve an improved communication data rate. Since most of the existing works focus on merely two-bit transmission in each pulse repetition interval (PRI) using different amplitude modulation (AM)- and phased modulation (PM)-based techniques, this paper proposes a new technique that doubles the data rate by using a hybrid FSK–FDM technique. Note that the AM-based techniques are used when the communication receiver resides in the side lobe region of the radar. In contrast, the PM-based techniques perform better if the communication receiver is in the main lobe region. However, the proposed design facilitates the delivery of information bits to the communication receivers with an improved bit rate (BR) and bit error rate (BER) regardless of their locations in the radar’s main lobe or side lobe regions. That is, the proposed scheme enables information encoding according to the transmitted waveforms and frequencies using FSK modulation. Next, the modulated symbols are added together to achieve a double data rate using the FDM technique. Finally, each transmitted composite symbol contains multiple FSK-modulated symbols, resulting in an increased data rate for the communication receiver. Numerous simulation results are presented to validate the effectiveness of the proposed technique.

A non-PMU-based WAN protection scheme for swing detection and stability enhancement in power systems

This paper presents a scheme for power swing detection and stability assessment for multi-machine power systems in addition to a brief survey on that topic. The scheme adopts smart grid perception. It is an asynchronous scheme that uses the rate of change of the angle of the generator bus voltage instead of synchronously estimating the absolute rotor angle value for stability assessment. Therefore, it does not require phasor measurement units (PMUs). Instead, it uses commercially available intelligent electronic devices (IEDs). It introduces a new application of the multicast Generic Object Oriented Substation Event (GOOSE) protocol or its routable version (R-GOOSE) to reflect the disturbance in the power grid on the transmission pattern of data packets over the grid data network. In this scheme, the angle change information is transmitted in the form of GOOSE packets simultaneously from the IEDs that readily measure the angles of the affected generators. On the other hand, the detecting device identifies the stability status of each affected generator. The proposed scheme is validated by simulating disturbances on the IEEE 39-bus system. The results show that the scheme can discriminate between stable and unstable swings. In addition, the loss of synchronism of the unstable generator is predicted ahead of its occurrence by enough time to take an appropriate action.