Need For Manual Adjustment Research Articles

Multiple strategies improved spider wasp optimization for engineering optimization problem solving

The Spider Wasp Optimization (SWO) algorithm is a swarm intelligence optimization technique inspired by the collective behaviors of social animals. This algorithm, designed to address optimization challenges, emulates the unique hunting, nesting, and mating behaviors of female spider wasps. It offers several advantages, including rapid search speed and high solution accuracy. However, when tackling complex optimization problems, it can encounter issues such as getting trapped in local optima, slow early convergence, and the need for manual adjustment of the “Trade-off Rate” (TR) parameter for different problems.To improve the performance and versatility of the SWO algorithm, a Multi-strategy Improved Spider Wasp Optimizer (MISWO) is proposed. Firstly, the Grey Wolf Algorithm is integrated into the initialization phase to enhance early convergence and improve the fitness of the initial population, thereby boosting the algorithm’s global optimization capabilities.Secondly, an adaptive step size operator and Gaussian mutation are introduced during the search phase to automatically adjust the search range at different optimization stages. This enhancement increases both the optimization accuracy and the algorithm’s ability to avoid local optima. The Trade-off Rate (TR) is dynamically selected to better accommodate a variety of problems. Finally, a dynamic lens imaging reverse learning strategy is employed to update optimal individuals, further improving the algorithm’s capacity to escape local optima. To validate the effectiveness of MISWO, it was tested on 23 benchmark functions and 7 engineering optimization problems, and compared with several state-of-the-art algorithms. Experimental results show that MISWO outperforms other algorithms in terms of optimization capability, stability, and adaptability across diverse problems.

A Hybrid Prediction Model for Short-Term Load Forecasting in Power Systems

Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation.

Identifying Community-Bridge Network Structures via Bayesian Learning With Mixed Sparsity Mode.

Identifying structures of complex networks based on time series of nodal data is of considerable interest and significance in many fields of science and engineering. This article presents a sparse Bayesian learning (SBL) method for identifying structures of community-bridge networks, where nodes are grouped to form communities connected via bridges. Using the structural information of such networks with unknown nodal dynamics and community formations, network structure identification is tackled similar to sparse signal reconstruction with mixed sparsity mode. The proposed method is theoretically proved to be convergent. Its superiority to mainstream baselines is demonstrated via extensive experiments without the need for manual adjustment of regularization parameters.

The effectiveness of granulometric composition image analysis in rock thin sections for the study of terrigenous reservoir rocks

Background. Image analysis of the granulometric composition in rock thin sections is an effective method that complements petrographic and sedimentation analyzes of reservoir rocks. However, due to imperfect research results and the specifics of rock structure, the application method has a number of limitations. Objective. The article shows the effectiveness of using image analysis for several groups of terrigenous rocks that differ in structural and mineral parameters. Materials and methods. The study was carried out using an optical microscope with the KERN S7 petrographic research analyzer. Results. The work outlines the scope and main limitations of image analysis. It also shows the need for manual adjustment of the image of certain types of rocks and control of the results using laboratory research methods. Conclusions. The results of the work will help to select a rational and effective complex for laboratory analysis of reservoir rocks, depending on their lithological features.

Meta-knowledge guided Bayesian optimization framework for robust crop yield estimation

Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic stability. The existing yield estimating models exhibit deficiencies in insufficient examination of hyperparameters, lack of robustness, restricted transferability of meta-models, and uncertain generalizability when applied to agricultural data. This study presents a novel meta-knowledge-guided framework that leverages three diverse agricultural datasets and explores meta-knowledge transfer in frequent hyperparameter optimization scenarios. The framework’s approach involves base tasks using LightGBM and Bayesian Optimization, which automates hyperparameter optimization by eliminating the need for manual adjustments. Conducted rigorous experiments to analyze the meta-knowledge transformation of RGPE, SGPR, and TransBO algorithms, achieving impressive R2 values (0.8415, 0.9865, 0.9708) using rgpe_prf meta-knowledge transfer on diverse datasets. Furthermore, the framework yielded excellent results for mean squared error (MSE), mean absolute error (MAE), scaled MSE, and scaled MAE. These results emphasize the method’s significance, offering valuable insights for crop yield estimation, benefiting farmers and the agricultural sector.

A bi-criteria approach to the truck-multidrone routing problem

In the rapidly expanding field of e-commerce logistics, the optimisation of last-mile delivery solutions is paramount. This paper introduces a novel methodology that addresses this challenge by generating approximate Pareto fronts in a hybrid truck-drone delivery system. Specifically, we examine a generalized single truck multi-drone problem that allows multi-visit flight missions and rendezvous points distinct from launch locations. Our goal is providing decision-makers with a portfolio of optimal routing solutions that balance service time and environmental impact, criteria that are increasingly shaping decision-making in this domain. To achieve this, we introduce a bivector coding scheme inspired by flow-shop scheduling problems and implement a Simulated Annealing algorithm. This algorithm features an advanced stopping mechanism, negating the need for manual adjustments by utilizing a distinctive blend of a domination rate and a Kalman filter. Importantly, our framework employs an iterated greedy search algorithm to evolve from initial solutions towards identifying non-dominated solutions sets, which are then ranked using a hypervolume coefficient. To validate our methodology, we conduct a sensitivity analysis on two different size instances using a full factorial design of experiments. Our analysis reveals crucial insights into the impact of the number of drones, their autonomy, and their flight speed settings. From it, we conclude that it is a robust and adaptable framework for its practical application for obtaining Pareto fronts solutions among which picking the ultimate routing to be implemented.

Online adaptive radiotherapy for bladder cancer using a simultaneous integrated boost and fiducial markers

PurposeThe aim was to assess the feasibility of online adaptive radiotherapy (oART) for bladder cancer using a focal boost by focusing on the quality of the online treatment plan and automatic target delineation, duration of the workflow and performance in the presence of fiducial markers for tumor bed localization.MethodsFifteen patients with muscle invasive bladder cancer received daily oART with Cone Beam CT (CBCT), artificial intelligence (AI)-assisted automatic delineation of the daily anatomy and online plan reoptimization. The bladder and pelvic lymph nodes received a total dose of 40 Gy in 20 fractions, the tumor received an additional simultaneously integrated boost (SIB) of 15 Gy. The dose distribution of the reference plan was calculated for the daily anatomy, i.e. the scheduled plan. Simultaneously, a reoptimization of the plan was performed i.e. the adaptive plan. The target coverage and V95% outside the target were evaluated for both plans. The need for manual adjustments of the GTV delineation, the duration of the workflow and the influence of fiducial markers were assessed.ResultsAll 300 adaptive plans met the requirement of the CTV-coverage V95%≥98% for both the boost (55 Gy) and elective volume (40 Gy). For the scheduled plans the CTV-coverage was 53.5% and 98.5%, respectively. Significantly less tissue outside the targets received 55 Gy in case of the adaptive plans as compared to the scheduled plans. Manual corrections of the GTV were performed in 67% of the sessions. In 96% of these corrections the GTV was enlarged and resulted in a median improvement of 1% for the target coverage. The median on-couch time was 22 min. A third of the session time consisted of reoptimization of the treatment plan. Fiducial markers were visible on the CBCTs and aided the tumor localization.ConclusionsAI-driven CBCT-guided oART aided by fiducial markers is feasible for bladder cancer radiotherapy treatment including a SIB. The quality of the adaptive plans met the clinical requirements and fiducial markers were visible enabling consistent daily tumor localization. Improved automatic delineation to lower the need for manual corrections and faster reoptimization would result in shorter session time.

PO-05-092 BIPOLAR ELECTROGRAM PEAK FREQUENCY VALUE DERIVED FROM HIGH-DENSITY ELECTROANATOMIC MAPPING EFFICIENTLY IDENTIFIES THE CRITICAL ISTHMUS IN SCAR-RELATED VENTRICULAR TACHYCARDIA

It is of challenges to differentiate local activities from far-field potentials, especially the multicomponent electrograms of diastolic potentials in scar-related reentrant ventricular tachycardia (VT). The peak frequency (PF) of each electrogram has the potential to discriminate near-field from far-field potentials. The study aimed to identify the VT isthmus by applying PF criteria on VT activation maps. The study consisted of 10 patients with scar-related VT (ischemic/nonischemic cardiomyopathy 6/4). The characteristics of the VT circuits were assessed using high-density multielectrode catheters. VT isthmuses were identified based on activation maps, entrainment, and ablation results. The accuracy of applying PF value over the activation map in detecting VT isthmus was assessed. 12 VT isthmuses were identified. The mean bipolar voltage and PF of the VT isthmuses were 0.26 ± 0.13 mV and 319.3 ± 58.6 Hz respectively. All isthmuses were within areas of bipolar voltage less than 0.5 mV and PF of more than 250 Hz. With the automated -dv/dtmax annotation during VT, the isthmus could be identified in 5 of 12 (41.7%) VT circuits without manual annotation. By automated PF annotation, the VT isthmus could be identified in 9 of 12 (75.0%) VT circuits without the need for manual adjustment. VT isthmus was always located in the electrogram with bipolar LVZ (<0.5 mV) with a PF value over 250Hz during VT. Moreover, the VT isthmus could be more intuitively identified by the automated peak frequency annotation than -dv/dtmax annotation in the current daily practice using high-density 3D electroanatomic mapping system.

Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study

PURPOSEThe aim was to evaluate a postprocessing optimization algorithm's ability to improve the spatial properties of a clinical treatment plan while preserving the target coverage and the dose to the organs at risk. The goal was to obtain a more homogenous treatment plan, minimizing the need for manual adjustments after inverse treatment planning. MATERIALS AND METHODSThe study included 25 previously treated prostate cancer patients. The treatment plans were evaluated on dose-volume histogram parameters established clinical and quantitative measures of the high dose volumes. The volumes of the four largest hot spots were compared and complemented with a human observer study with visual grading by eight oncologists. Statistical analysis was done using ordinal logistic regression. Weighted kappa and Fleiss’ kappa were used to evaluate intra- and interobserver reliability. RESULTSThe quantitative analysis showed that there was no change in planning target volume (PTV) coverage and dose to the rectum. There were significant improvements for the adjusted treatment plan in: V150% and V200% for PTV, dose to urethra, conformal index, and dose nonhomogeneity ratio. The three largest hot spots for the adjusted treatment plan were significantly smaller compared to the clinical treatment plan. The observers preferred the adjusted treatment plan in 132 cases and the clinical in 83 cases. The observers preferred the adjusted treatment plan on homogeneity and organs at risk but preferred the clinical plan on PTV coverage. CONCLUSIONSQuantitative analysis showed that the postadjustment optimization tool could improve the spatial properties of the treatment plans while maintaining the target coverage.

Applicability of a semiautomated volumetric approach (5D CNS+™) for detailed antenatal reconstruction of abnormal fetal CNS anatomy

BackgroundThe aim of this study was to evaluate the accuracy and reliability of a semiautomated volumetric approach (5D CNS+™) when examining fetuses with an apparent abnormal anatomy of the central nervous system (CNS).MethodsStored 3D volumes extracted from a cohort of > 1.400 consecutive 2nd and 3rd trimester pregnancies (range 15–36 gestational weeks) were analyzed using the semiautomatic software tool 5D CNS+™, enabling detailed reconstruction of nine diagnostic planes of the fetal brain. All 3D data sets were examined and judged for plane accuracy, the need for manual adjustment, and fetal CNS anomalies affecting successful plane reconstruction.ResultsBased on our data of 91 fetuses with structural cerebral anomalies, we were able to reveal details of a wide range of CNS anomalies with application of the 5D CNS+™ technique. The corresponding anatomical features and consecutive changes of neighboring structures could be clearly demonstrated. Thus, a profound assessment of the entire altered CNS anatomy could be achieved in nearly all cases. The comparison with matched controls showed a significant difference in volume acquisition (p < 0.001) and in need for manual adjustment (p < 0.001) but not in the drop-out rates (p = 0.677) of both groups.Conclusion5D CNS+™ is applicable in the majority of cases with brain lesions and constitutes a reliable tool even if the integrity of the fetal CNS is compromised by structural anomalies. Using volume data that were acquired in identical cutting sections needed for conventional biometry allows for detailed anatomic surveys grossly independent of the examiner’s experience.

Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring.

To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.

MO-0549 No need for manual adjustments of deep learning segmentation in oropharyngeal cancer?

MO-0549 No need for manual adjustments of deep learning segmentation in oropharyngeal cancer?

Gradient Descent Method With Multiple Adaptive Step Sizes for Identifying Loudspeaker Nonlinearities

An improved gradient descent method with multiple variable step sizes for identifying nonlinear parameters of moving-coil loudspeakers is proposed. This method monitors parameters trends during identification and adaptively multiplies or attenuates corresponding step sizes, eliminating the need for manual adjustments. Simulations and experiments reveal that it is as accurate as the fixed step size method and more efficient and robust against initial linear parameter errors.

Sources and steps of corpus lemmatization

Abstract This article describes the steps and results of the lemmatization of the derived anomalous verbs of Old English. The data have been retrieved from The Dictionary of Old English Web Corpus, searched through the lexical database from the Nerthus Project called Norna. The methodology comprises several steps combining automatic searches on the lemmatizer and manual revision. Part of the results, including the verbs starting with the letters A to H, are compared with the Dictionary of Old English, while the rest of the lemmas are checked with the standard Old English dictionaries (Clark-Hall, Sweet and Bosworth-Toller). The discussion leads to the conclusion that the lemmatization of the verbs of Old English, a language with a remarkable degree of spelling variation, requires considerable manual revision. However, the progressive improvement of automatic searches, based on the comparison of the initial results with the available lexicographical sources, minimizes the need for manual adjustment.

A metamodel for building information modeling-building energy modeling integration in early design stage

Full interoperability between BIM and energy simulation tools has not yet been achieved. Performance prediction metamodels can help to overcome this issue and serve as an investigation tool in the early design stage. This paper describes the development and validation of a tool to integrate building information models (BIMs) and a previously developed metamodel to predict the thermal load of dwellings through gbXML. The results obtained show the feasibility of developing the proposed tool. However, inconsistencies between gbXML files from different BIM authoring tools were observed. There was a need for manual adjustments to the building information models before export to gbXML. This finding reinforces the need for the standardized implementation of exportation tools for open interoperability formats by software manufacturers. It also indicates the possibility for the future use of this tool to support the Brazilian energy labeling certification process and assist building designers in the early design stage.

Validation of a semiautomated volumetric approach for fetal neurosonography using 5DCNS+ in clinical data from >\u20091100 consecutive pregnancies

ObjectiveThe aim of this study was to evaluate the validity of a semiautomated volumetric approach (5DCNS+) for the detailed assessment of the fetal brain in a clinical setting.MethodsStored 3D volumes of > 1100 consecutive 2nd and 3rd trimester pregnancies (range 15–36 gestational weeks) were analyzed using a workflow-based volumetric approach 5DCNS+, enabling semiautomated reconstruction of diagnostic planes of the fetal central nervous system (CNS). All 3D data sets were examined for plane accuracy, the need for manual adjustment, and fetal-maternal characteristics affecting successful plane reconstruction. We also examined the potential of these standardized views to give additional information on proper gyration and sulci formation with advancing gestation.ResultsBased on our data, we were able to show that gestational age with an OR of 1.085 (95% CI 1.041–1.132) and maternal BMI with an OR of 1.022 (95% CI 1.041–1.054) only had a slight impact on the number of manual adjustments needed to reconstruct the complete volume, while maternal age and fetal position during acquisition (p = 0.260) did not have a significant effect. For the vast majority (958/1019; 94%) of volumes, using 5DCNS+ resulted in proper reconstruction of all nine diagnostic planes. In less than 1% (89/9171 planes) of volumes, the program failed to give sufficient information. 5DCNS+ was able to show the onset and changing appearance of CNS folding in a detailed and timely manner (lateral/parietooccipital sulcus formation seen in < 65% at 16–17 gestational weeks vs. 94.6% at 19 weeks).ConclusionsThe 5DCNS+ method provides a reliable algorithm to produce detailed, 3D volume–based assessments of fetal CNS integrity through a standardized reconstruction of the orthogonal diagnostic planes. The method further gives valid and reproducible information regarding ongoing cortical development retrieved from these volume sets that might aid in earlier in utero recognition of subtle structural CNS anomalies.

Quasi-automated reconstruction of the femur from bi-planar X-rays

ABSTRACT 3D reconstruction from low-dose Bi-Planar X-Rays (BPXR) is a rising practice in clinical routine. However, this process is time consuming and highly depends on the user. This study aims to partially automate the process for the femur, thus decreasing reconstruction time and increasing robustness. As a training set, 50 femurs are segmented from CT scans together with 120 BPXR reconstructions. From this dataset, an initial solution for the bony contours is defined through Gaussian Process Regression (GPR), using eight digitized landmarks. This initial solution is projected on both x-rays and automatically adjusted using an adapted Minimal Path Algorithm (MPA). To evaluate this method, CT-scans were acquired from 20 cadaveric femurs. For each sample, the CT-based reconstruction is compared to the one automatically generated from the digitally reconstructed radiographs. Euclidean distances between femur reconstructions and the segmented CT data are on average 1.0 mm with a Root Mean Square Error (RMSE) of 0.8 mm. Femoral torsion errors are assessed: the bias is lower than 0.1° with a 95% confidence interval of 4.8°. The proposed method substantially improves 3D reconstructions from BPXR, as it enables a fast and reliable reconstruction, without the need for manual adjustments, which is essential in clinical routine.

A Nonparametric Approach to Estimating Heterogeneous Demand from Censored Sales Panel Data

Analyzing historical sales data to draw conclusions on the underlying demand structure is a central foundation for sales planning, e.g. in assortment and revenue optimization. This contribution focuses on estimating the choice behavior of demand segments as well as their distribution from panel data featuring multiple consecutive sales observations. Existing methods in this area mostly utilize parametric models and estimation procedures that rely on some given information, i.e., expert knowledge. To overcome this requirement, we employ finite mixtures to model sales events over multiple time frames and obtain nonparametric demand estimators. The proposed approach requires no given assumptions over underlying distributions. Furthermore, we also introduce a hindsight approach to assign individual sales observations to demand segments. This contribution decreases the need for manual adjustments in demand estimation and allows practitioners to gain detailed insight in purchase behaviors. In an extensive simulation study, we benchmark the approach on different data sets and compare its results to those from published approaches. The study highlights that the approach shows superior performance for markets with heterogeneous demand.

Discretely programmable microwave photonic filter based on temporal Talbot effects.

We propose and experimentally demonstrate a reconfigurable microwave photonic filter based on temporal Talbot effects. The microwave signal is first uniformly sampled by a train of optical pulses through electro-optic intensity modulation. The sampled optical pulses are then directed to a Talbot-based optical signal processor, consisting of an electro-optic temporal phase modulator and a chromatic dispersion line. The Talbot-based microwave photonic filter (TMPF) exploits the inherent properties of the Talbot self-imaging effect for mitigating pulse-to-pulse intensity fluctuations of optical pulses to transmit some fluctuation frequencies and mitigate or entirely block other microwave spectral components. The output microwave signal is finally reconstructed from the processed optical pulses and the resultant RF response is measured by a network analyzer. The TMPF exhibits an RF response with periodic, symmetric-profile passbands whose center frequency and free spectral range (FSR) are defined by the sampling rate and the dispersion value. The filter passbands can be reconfigured electrically, in discrete steps, by adjusting the modulation function of the phase modulator, i.e., without the need for manual adjustment of the optical components. This enables the capability of selection of specific passbands among the primary passbands. The phase modulation function is provided using an arbitrary waveform generator, with the potential for fast tuning of the filter's spectral response. The bandwidth of the filter passband can also be easily customized by adjusting the sampling pulse's temporal width using an optical bandpass filter. Examples of filter performance in various passband configurations are also presented in the time domain to further validate the operation of the filter.

Simplex-based navigation tool for a posteriori selection of the preferred deformable image registration outcome from a set of trade-off solutions obtained with multiobjective optimization for the case of breast MRI.

Multiobjective optimization approaches for deformable image registration (DIR) remove the need for manual adjustment of key parameters and provide a set of solutions that represent high-quality trade-offs between objectives of interest. Choosing a desired outcome a posteriori is potentially far more insightful as differences between solutions can be immediately visualized. The purpose of this work is to investigate whether such an approach allows clinical experts to intuitively select their preferred DIR outcome. To this end, we developed a simplex-based tool for solution navigation and asked 10 clinical experts to use it to choose their preferred DIR outcome from sets of trade-off solutions obtained for 10 breast magnetic resonance DIR cases of low (prone-prone DIR; ) and high (prone-supine DIR; ) difficulty, of patients and volunteers, respectively. The usability of the software is subsequently evaluated by the observers using the system usability scale. Further, the quality of the selected DIR outcomes is evaluated using the mean target registration error. Results show that the users are able to identify and select high-quality DIR outcomes, and attested to high learnability and usability of our software, supporting the validity of the presumed added value of taking a multiobjective perspective on DIR in clinical practice.