Mode Selection Research Articles

Recent Developments of Hybrid Fluorescence Techniques: Advances in Amyloid Detection Methods.

Amyloid fibrils are formed from various pathological proteins. Monitoring their aggregation process is necessary for early detection and treatment. Among the available detection techniques, fluorescence is simple, intuitive, and convenient due to its sensitive and selective mode of detection. It has certain disadvantages like poor photothermal stability and detection state limitation. Research has focused on minimising the limitation by developing hybrid fluorescence techniques. This review focuses on the two ways fluorescence (intrinsic and extrinsic) has been used to monitor amyloid fibrils. In intrinsic/label free fluorescence: i) The fluorescence emission through aromatic amino acid residues like phenylalanine (F), tyrosine (Y) and tryptophan (W) is present in amyloidogenic peptides/protein sequence. And ii) The structural changes from alpha helix to cross-β-sheet structures during amyloid formation contribute to the fluorescence emission. The second method focuses on the use of extrinsic fluorophores to monitor amyloid fibrils i) organic dyes/small molecules, ii) fluorescent tagged proteins, iii) nanoparticles, iv) metal complexes and v) conjugated polymers. All these fluorophores havetheir own limitations. Developing them into hybrid fluorescence techniques and converting it into biosensors can contribute to early detection of disease.

Resource allocation optimization of device-to-device communication using machine learning algorithms

Device-to-device communication is envisioning next-generation wireless communication. The utility of the device-to-device communication model encourages emerging communication systems such as 5G and the Internet of Things. The allocation of resources and channel interference are major challenges in device-to-device communication. This paper proposes machine-learning-based algorithms for resource allocation and optimization of device-to-device communication. The proposed machine learning algorithm is a cascaded support vector machine. The cascaded support vector machine mapped the parameters of CUEs and DUEs. We create an iterative algorithm to achieve low-power, energy-efficient resource allocation with mode selection by formulating a novel optimization problem to maximize energy efficiency using the subtractive form method to solve a fractional objective function. We obtain data samples from a suboptimal algorithm to train the cascaded algorithm and verify the trained algorithm. Our numerical results show that the proposed cascaded machine learning-based transmission algorithm's accuracy reaches about 88%–95% despite its simple structure due to the limitation in computing power. The analysis of results suggests that the proposed algorithm is more efficient than SVM, DSVM, and the reinforcement learning (RL) algorithm.

Editorial

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six accepted papers involving 17 authors from six different countries (Argentina, Brazil, Ecuador, Hong Kong, India, Saudi Arabia). Álex dos Santos Moura, Fábio Gomes Rocha, and Michel S. Soares from Brazil propose in their research a recommendation tool based on information retrieval to assist developers in choosing the suitable microservices pattern to solve a given problem. In a collaborative research effort between Ecuador and Argentina, Gonzalo P. Espinel-Mena, José L. Carrillo-Medina, Eddie E. Galarza, and Mario Matias Urbieta discuss a systematic mapping of configuration management activities in software product line. Edward Kai Fung Dang, Robert Wing Pong Luk, and Qing Li from Hong Kong contribute to the forum for negative results with their study on word bigrams for pseudo-relevance feedback in information retrieval. Prateek Thakral and Yugal Kumar from India present in their work an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. Samit Bhanja, Banani Ghose, and Abhishek Das from India highlight their findings on multi-step-ahead time series forecasting using a deep learning and fuzzy time series-based error correction method. And last but not least, Abdullilah A. Alotaibi and Salman A. AlQahtani from Saudi Arabia present in their research an intelligent distributed channel selection framework with hybrid mode selection for interference mitigation in D2D based 5G networks.  Enjoy Reading! Best regards,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks

Device-to-device (D2D) communication is an essential part of the 5G system used in cellular networks, which enables devices to communicate with each other without passing the base station BS. It is likely to aid in satisfying increasing capacity and effectively offloading traffic from the BS by distributing the transmission between D2D users from one side and cellular users and the BS from the other side. It results in improved throughput, energy efficiency, and other parameters. At the same time, the introduction of D2D communication in cellular networks creates new technical challenges to be addressed. One of the major issues is the mitigation of interference between device users (DUs) and primary users (PUs). This issue leads to performance degradation if it is not managed properly. In this paper, an intelligent distributed channel selection framework (IDCSF) in D2D communication for 5G networks with hybrid selection modes of D2D is proposed to help DUs select the best channel for transmission to mitigate interference. This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. Furthermore, PU activity model is adjusted to aid in recovering from bad channel selection decisions. Moreover, fuzzy logic technique is utilized to determine the reliable channels which can help the DU along with channel selection algorithm to find channel’s weight values. The channel with highest weight value is selected as the best channel. Simulation results show that the proposed framework IDCSF enhances selecting the best channel, improves throughput and spectral utilization, and reduces average delay, interference and search time compared to other approaches.

Experimental Analysis of Freehand Multi-object Selection Techniques in Virtual Reality Head-Mounted Displays

Object selection is essential in virtual reality (VR) head-mounted displays (HMDs). Prior work mainly focuses on enhancing and evaluating techniques for selecting a single object in VR, leaving a gap in the techniques for multi-object selection, a more complex but common selection scenario. To enable multi-object selection, the interaction technique should support group selection in addition to the default pointing selection mode for acquiring a single target. This composite interaction could be particularly challenging when using freehand gestural input. In this work, we present an empirical comparison of six freehand techniques, which are comprised of three mode-switching gestures (Finger Segment, Multi-Finger, and Wrist Orientation) and two group selection techniques (Cone-casting Selection and Crossing Selection) derived from prior work. Our results demonstrate the performance, user experience, and preference of each technique. The findings derive three design implications that can guide the design of freehand techniques for multi-object selection in VR HMDs.

Agency mode selection and software competition in supply chains

AbstractAt present, many software developers work with downstream service providers to sell software and services. For political or economic reasons, foreign developers may be not allowed to sell software and services directly in the country of providers, but they can authorize their software to the providers for sale. In this study, we examine two types of IT supply chain (ITSC), namely, “Agenting Domestic Software” (Mode 1) and “Agenting Foreign Software” (Mode 2), each with a domestic developer/a foreign developer, a service provider, and client enterprises. In both modes, clients can either pay a high price to purchase software with accessorial pre‐sale service from the provider, or firsthand acquire it from the domestic developer at a low price but without pre‐sale service. We observe that in Mode 1, the domestic developer's market scale and the competition intensity of software have no effect on the domestic developer's decisions and profit; however, they have influences on the provider's decisions and profit. In Mode 2, the profits of ITSC members may increase with the intensity of software competition. When the domestic developer's market scale is small (large), and the domestic developer has (loses) the advantage of low extended‐warranty‐service (EWS) cost, Mode 1 (2) is more beneficial to the domestic developer than Mode 2 (1); however, the provider is more willing to choose Mode 2 (Mode 1). In other cases, either Mode 1 or Mode 2 may result in a win‐win situation for the domestic developer and provider.

Self-assembled micro-patterns in uphill-diffusion solution system.

In this work we present self-organized regular patterns in a solution system through uphill-diffusion. Micrometer thick organic semiconductor solution is sandwiched between a substrate and cover-plate. Self-assembled regular patterns can be observed on the substrate after solvent evaporation. Different micro-patterns and pattern defects were displayed and analyzed. Mechanisms of defect formation, mode selection process during patten generation, and pattern sedimentation onto substrate from solution were proposed. Organic thin film transistors were fabricated with the assembled line patterns which demonstrate a promising way to produce patterned micro/nano materials.

Theoretical analysis of heat and mass transfer characteristics of a counter-flow packing tower with different heating modes

Mass extraction/injection and multi-stage system have been proven to be effective approaches to improve the performance of humidification and dehumidification (HDH) desalination system. However, these two optimization schemes cause that the flow ratios of water to air at different positions of humidifier or that of different humidifiers have great difference. A single heating mode is inapplicable to all flow ratios of water to air. Hence, it is necessary to reveal the relationship among mass flow ratio, heating mode and thermal efficiency of humidifier for the selection of appropriate heating mode. A theoretical model of packing tower (typical kind of humidifier) which agrees well with experimental results was established in this paper, and the average relative errors of outlet air temperature, humidity and thermal efficiency are 2.3 %, 4.9 % and 5.8 %, respectively. A comparison among the thermal efficiencies of a counter-flow packing tower with different heating modes (I: solution heated, II: air heated, III: solution-air heated) was conducted. It is found that there exist two values of mass flow ratio (FRI-II and FRII-III) at which the thermal efficiencies of air heated and solution-air heated are equal and that of solution heated and solution-air heated are equal, respectively. However, the thermal efficiency of solution heated is always higher than that of solution-air heated (FRI-III does not exist). It seems clear that there is a relationship between FR and thermal efficiency (η) as follows: FR < FRI-II, ηI > ηIII > ηII; FRI-II < FR < FRII-III, ηI > ηII > ηIII; FR > FRI-II, ηII > ηI > ηIII. Additionally, the effects of inlet parameters on the FRI-II and FRII-III are also evaluated. The results show that the maximum temperature, inlet solution concentration and ambient temperature have positive effects on FRI-II and FRII-III, but ambient relative humidity has negative effect. In the final part, the selection of the heating mode of a counter-flow packing tower is taken as example to introduce the application of FRI-II and FRII-III proposed in this paper.

Random Raman Lasing in Diode-Pumped Multi-Mode Graded-Index Fiber with Femtosecond Laser-Inscribed Random Refractive Index Structures of Various Shapes

Diode-pumped multi-mode graded-index (GRIN) fiber Raman lasers provide prominent brightness enhancement both in linear and half-open cavities with random distributed feedback via natural Rayleigh backscattering. Femtosecond laser-inscribed random refractive index structures allow for the sufficient reduction in the Raman threshold by means of Rayleigh backscattering signal enhancement by +50 + 66 dB relative to the intrinsic fiber level. At the same time, they offer an opportunity to generate Stokes beams with a shape close to fundamental transverse mode (LP01), as well as to select higher-order modes such as LP11 with a near-1D longitudinal random structure shifted off the fiber axis. Further development of the inscription technology includes the fabrication of 3D ring-shaped random structures using a spatial light modulator (SLM) in a 100/140 μm GRIN multi-mode fiber. This allows for the generation of a multi-mode diode-pumped GRIN fiber random Raman laser at 976 nm with a ring-shaped output beam at a relatively low pumping threshold (~160 W), demonstrated for the first time to our knowledge.

Cooperation mode selection strategies in platform ecosystems: analyzing brand value, cross-selling, and platform empowerment

Purpose This paper aims to investigate the vertical cooperative relationship between the core enterprise and the manufacturer within the platform ecosystem, specifically analyzing the optimal decision-making processes of both parties under the original equipment manufacturer (OEM) and original brand manufacturer (OBM) modes. Design/methodology/approach This paper uses game theory to analyze the problem, considering factors such as brand value difference, cross-selling and platform empowerment. It constructs the game models for both OEM and OBM modes and discusses the selection strategies for the cooperation mode. Findings The results indicate that the choice of cooperation mode by the manufacturer and the core enterprise depends on the relative size of their brand values. In cases of inconsistent choices, cooperation can be improved by designing a transfer payment contract. When the brand value is constant, the product price is comprehensively affected by cross-selling revenue, price elasticity coefficient, cost coefficient of sales effort and cost coefficient of platform empowerment. The enterprise reduces the price only when the potential revenue brought by increasing product sales exceeds the marginal profit brought by increasing product pricing; otherwise, it raises the sales price. Originality/value The platform ecosystem is emerging as a future direction for business mode development. However, there is a paucity of research on the cooperation modes between manufacturers and core enterprises within the platform ecosystem.

Generation of High-Quality Cylindrical Vector Beams from All-Few-Mode Fiber Laser

Transverse mode control of laser intracavity oscillation is crucial for generating high-purity cylindrical vector beams (CVBs). We utilized the mode conversion and mode selection properties of two-mode long-period fiber gratings (TM-LPFGs) and two-mode fiber Bragg gratings (TM-FBGs) to achieve intracavity hybrid-mode oscillations of LP01 and LP11 from an all-few-mode fiber laser. A mode-locked pulse output with a repetition rate of 12.46 MHz and a signal-to-noise ratio of 53 dB was achieved with a semiconductor saturable absorber mirror (SESAM) for mode-locking, at a wavelength of 1550.32 nm. The 30 dB spectrum bandwidth of the mode-locked pulse was 0.13 nm. Furthermore, a high-purity CVB containing radially polarized and azimuthally polarized LP11 modes was generated. The purity of the obtained CVB was greater than 99%. The high-purity CVB pulses have great potential for applications in optical tweezers, high-speed mode-division multiplexing communication, and more.

Thermal properties of mullite‐type SnAlBO4 and SnGaBO4

AbstractWe report on temperature‐dependent structural and spectroscopic properties of two new members of the mullite‐type ceramics SnAlBO4 and SnGaBO4. In‐situ X‐ray powder diffraction (XRPD) demonstrates positive thermal expansion behavior for all orthorhombic lattice parameters between 13 and 840 K. The lattice thermal expansion is modeled by Grüneisen first‐order approximation, where the vibrational energy is calculated by the Debye–Einstein–anharmonicity (DEA) approach. Although the thermal changes of the metric parameters do not show any discontinuity, the double‐Debye model and low‐temperature thermal analysis leave hints for subtle displacive changes. Splitting of the tin‐doublets of the 119Sn Mössbauer spectra at low temperature is assumed to be associated with structural modulation although the temperature‐dependent Raman spectra could not support these findings. The modulation could either be dynamic which requires much longer thermal equilibration than the speed of the data collection for XRPD and Raman spectroscopy. Selective Raman mode frequencies are analyzed using a modified Klemens model, which helps to understand the thermal anharmonic behaviors of the SnO4, MO6, and BO3 polyhedra as a function of temperature.

Brand livestream mode options: AI vs KOL livestream in the platform supply chain

PurposeDriven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.Design/methodology/approachThis paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.FindingsSeveral interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.Originality/valueThis study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.

Covert Communications in a Hybrid DF/AF Relay System.

In this paper, we study covert communications in a hybrid decode-and-forward (DF)/ amplify-and-forward (AF) relay system. The considered relay in normal operation forwards messages from a source node to a destination node in either DF or AF mode on request. Meanwhile, the source and destination nodes also attempt to secretly exchange covert messages such as confidential or sensitive information and avoid detection by the covert message detector embedded on the relay. We first establish an optimal DF/AF mode selection criterion to maximize the covert rate based on the analyses of delay-aware achievable covert rates of individual DF and AF modes. To further reduce the time complexity, we propose a low-complexity selection criterion as well for practical use. The numerical results demonstrate the covert rate gain as high as 50% and running time gain as high as 20% for particular system parameters, which verify the effectiveness of the proposed criteria.

Systematic Assessment of Practical Challenges in Rural Domestic Sewage Treatment in China: Examining Treatment Models, Ecological Risks, and Management Dilemmas

The treatment and purification of rural domestic sewage (RDS) is a pivotal focus in enhancing the living environment in rural areas. Since 2008, special funds for comprehensive rural environmental improvement have been established by the Chinese government. Numerous projects have been implemented to treat RDS, resulting in a significant enhancement of China’s rural sewage treatment ratio. However, current discussions often focus on technical investigations, process selection, and operation modes pertaining to urban sewage while overlooking the unique challenges posed by decentralized sewage treatment facilities in rural areas. This work aims to provide technical support for addressing rural sewage treatment and purification in China through an analysis of limitations associated with prevailing mainstream sewage treatment and separation technologies, ecological risks arising from new pollutants present in domestic wastewater, and subsequent management difficulties.

ANALYSIS OF THE ENERGY EFFICIENCY OF TECHNICAL SOLUTIONS OF THE HEAT-DRIVEN SHIP EJECTOR REFRIGERATION MACHINE FOR OBTAINING SUB-ZERO TEMPERATURES

Marine refrigeration machines are responsible for large amounts of electricity consumption, as well as direct emissions of high global warming potential (GWP) refrigerants, so they need to be gradually upgraded. The possibility of using a ship ejector refrigeration machine with two-stage compression, consuming waste heat, as well as a cascade compression-ejector refrigeration machine to obtain the temperatures of minus 28–27 °C was analyzed. It was shown that the traditional indicator of the efficiency of heat-driven refrigeration machines COPtherm ot be recommended for the selection of energy-efficient modes of operation of ejector refrigeration machines that consume waste heat. As a criterion for the energy efficiency of ejector refrigeration machines that consume waste heat, it is proposed to analyze COPmechTot, which takes into account the electric power of pumps, fans, as well as units that provide “feeding” the refrigeration machine with waste heat. A two-stage ejector refrigeration machine that consumes waste heat with a temperature of 95–45 °C and is intended for the operation of a ship ice generator (boiling temperature — 28 °C) loses to a traditional vapor compression refrigeration machine in terms of energy consumption: COPmechTot = 1.266 vs. COPmechTot = 1.52. A cascade vapor compression ejector refrigeration machine that consumes waste heat with a temperature of 85–95 °C and is designed to provide refrigeration (boiling temperature — 27 °C) of ship provision chambers is more attractive than a traditional vapor compression one: COPmechTot = 2.37 vs. COPmechTot = 1.82. An alternative technical solution for the production of sub-zero temperatures on ships, which the authors plan to consider in further research, is a refrigeration machine with two-stage compression. It uses a compressor in the first stage and an ejector in the second stage. Bibl. 26, Fig. 7, Tab. 1.

8372 Evidence of dACC Glutamate Modulation During Top-down Inhibitory Control Predicting Weight Loss Ability in Individuals with Obesity Using1H fMRS

Abstract Disclosure: J.M. Eichstaedt: None. R. Sochocki: None. D. Khatib: None. N. Miller: None. V.A. Diwadkar: None. P. Burghardt: None. A.E. Rothberg: None. J.A. Stanley: None. Poor weight loss maintenance (WLM) following dietary intervention has been associated with poor top-down inhibitory control. The neurobiological mechanisms are not fully understood; it is not clear if changes to inhibitory control are specific to the presence of appetitive food cues. The dorsal anterior cingulate cortex (dACC) is central to inhibitory control and can be conceptualized selectively withholding ‘prepotent’ motor responses. This is driven by highly integrated glutamatergic and GABAergic neurotransmission. Temporal changes in glutamate, reflecting shifts in glutamate neurotransmission, can be detected under distinct processes or task conditions using [1]H fMRS. We investigated differences in dACC glutamate under task conditions with or without inhibitory motor control, utilizing neutral Squares and Food/Non-food stimuli, and their association with weight loss. Data were acquired from 13 individuals with obesity following dietary intervention resulting in 10% weight loss (5F; mean age: 50.1±5.2yrs). A visually-guided motor tapping task was used requiring participants to respond to stimuli under two modes, “Non-Selective” (motor responses to 100% of trials) and “Selective” (withholding responses on 20% of trials - involving both motor control and inhibition), and under two stimuli types (Squares and appetitive Food/Non-Food cues). Glutamate modulation changes (relative to the total signal) were assessed across stimuli type (independent of task mode) using repeated measures generalizing estimating equations (GEE) approach (SAS GENMOD). The correlation between glutamate during the Selective mode with food/non-food stimuli and percentage of weight lost during the dietary intervention was also assessed. The stimuli model term was not significant (χ2=3.50, p=0.174), but post hoc analysis indicated significantly increased glutamate during the Squares (z=-1.99, p=0.046), but not the Food/Non-food (z=-1.39, p=0.163) relative to baseline. A trending correlation between decreasing dACC glutamate modulation (Selective mode, Food/Non-Food) and decreasing percentage of weight lost during the intervention was observed (R2=0.274, F(1,11)=4.15, p=0.07). This suggests that the presence of appetitive food cues leads to a non-significant change in the dACC glutamate modulation independent of inhibitory or motor alone responses. The potential correlation between dACC glutamate modulation and weight loss provides preliminary support that appetitive food cues influence inhibitory control and the extent of the influences may be related to one’s ability to maintain weight loss. These results could provide greater understanding of the neurobiological mechanisms underlying top-down inhibitory processes influenced by affect and difficulties with weight loss and maintenance, potentially fueling better treatment for successful long-term WLM in obesity. Presentation: 6/1/2024

In-depth research on fault diagnosis of turbine rotor utilizing NGSABO-optimized VMD and CNN-BiLSTM

Abstract To solve the problem of difficulty in extracting and identifying fault types during turbine rotor operation, a fault diagnosis method based on improved subtraction mean optimizer (NGSABO) algorithm to optimize variational mode decomposition (VMD) and CNN-BiLSTM neural network is proposed. Firstly, three improvements are made to the subtraction average optimizer algorithm. Secondly, the optimal VMD parameter combination of NGSABO adaptive selection mode decomposition number K and penalty factor is used to decompose the rotor fault signal, and the minimum sample entropy is used as the fitness function for feature extraction. Combining convolutional neural network and bidirectional long short-term memory network to identify and classify features. Compared with other methods, this method has outstanding performance in the diagnosis of single and coupled rotor faults. The accuracy of fault diagnosis reaches 98.5714%, which has good practical application value.&amp;#xD;

Supervisory control strategy for dual battery assisted solar water pumping using a 3-stage interleaved boost converter

This article proposes effective power management and battery protection strategies for a solar photovoltaic (SPV) powered dual-battery supported standalone water pumping approach propelled by a permanent magnet brushless direct current (PMBLDC) motor. A drift-avoidance perturb and observe MPPT algorithm is employed in conjunction with a 3-stage interleaved boost converter (TSIBC) and proposes a switching control scheme to enable double-stage solar photovoltaic generation system (SPGS) to operate at its maximum accessible power point (MPP) irrespective of any variation in climatic condition. The proposed dual battery control scheme (DBCS), which integrates a primary battery with an auxiliary battery, is connected to the 310 V DC link. This dual battery setup utilizes a parallel-active configuration facilitated by two bidirectional DC-DC converters (BDCs). The core idea behind dualization aims to maintain the state of charge (SoC) of the primary battery within upper and lower limits with the help of an auxiliary battery unit and regulate the DC link voltage at 310 V during fluctuations generated in the protection period. This scheme significantly extends the battery's lifespan, achieving an estimated 800 to 1000 cycles by maintaining its depth of discharge (DoD) at a level of 70 %. A centralized power handling unit (CPHU) is integrated for both the BDCs to facilitate the operating mode selection and regulate DC link voltage by alternatively charging/discharging both batteries. This approach enhances overall system efficiency by eliminating the need to oversize the solar array by up to 46 %, which would have been necessary to fully charge both batteries simultaneously. Previously, the DC link voltage regulation relied solely on MPPT control, resulting in significant deviations (up to ±38.43 %) during battery overcharging/deep-discharging (SoC ≥ 90 %/SoC ≤ 20 %). This work proposes a solution using an auxiliary battery controller within the DBCS, achieving near-zero (±0.5 %) DC link voltage deviation under all operating conditions. The effectiveness of the suggested DBCS controller is validated through a time-domain simulation conducted in MATLAB/Simulink, alongside real-time hardware-in-the-loop (HIL) digital simulator using the OPAL-RT platform. The acquired results corroborate enhanced performance for the standalone water pumping system (WPS) and demonstrate a seamless shift from the normal operational mode to battery protection mode, while ensuring steady regulation of the DC link voltage.

Multi-mode high resolution TFM imaging of microdefects based on laser ultrasonic full matrix capture

The identification and visualization of submillimeter microdefects have long been a critical focus within the realm of ultrasonic testing. The utilization of high-frequency signals is imperative due to the small acoustic interaction structure. An additional benefit of using lasers is their non-contact nature and ability to utilize multiple imaging modes, effectively reducing false or missed detections and enhancing credibility. This study proposes an advanced optimization algorithm for imaging and quantitative evaluation of submillimeter microdefects using laser ultrasonic full matrix capture (FMC) and total focusing method (TFM). The analysis of acoustic directionality under laser excitation and detection provides a foundation for mode selection. In preprocessing, the multi-scale principal component analysis (MSPCA) method is introduced to suppress coherent noise and blind spots. Moreover, phase coherence methods are implemented to further reduce mode artifacts, background noise, and residual surface acoustic wave (SAW). Additionally, multi-mode imaging and mode fusion are achieved using reflected longitudinal waves, reflected shear waves, converted longitudinal waves, and converted shear waves. Experiments were carried out on two different types of microdefects, namely side drilled holes (SDHs) and blind holes (BHs), resulting in high resolution TFM imaging with a minimum BH size of 0.3 mm. The outcomes demonstrate the efficacy of MSPCA in suppressing coherent noise, with further enhancement in imaging quality after phase coherence weighting. In addition, the −6 dB threshold of multi-mode images also provided the size information. This research is poised to contribute to the resolution of quality assessment challenges in powder metallurgy, welding, and additive manufacturing processes.