Local Ability Research Articles

A novel combined intelligent algorithm prediction model for the risk of the coal and gas outburst

The mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the GWO, Tent Chaotic Mapping and DLH strategies were introduced to improve the optimization ability of the GWO and reduce the local optimal probability. The improved prediction model IGWO-SVM was used to predict the coal and gas outburst. The results showed that this model has faster training speed and higher classification prediction accuracy than the SVM and GWO-SVM models, which the accuracy rate reaching 100%. Finally, to obtain the correlation between the parameters of the coal and gas outburst prediction parameters, the random forest algorithm was used for training, and the three parameters with the highest feature importance were selected to rebuild the data set for machine learning. The accuracy of the IGWO-SVM outburst prediction model based on Random Forest was still 100%. Therefore, even if some prediction parameters are missing, the outburst can still be effectively predicted by using the RF-IGWO-SVM model, which is beneficial for the model application and underground safety management.

Multi-strategy Improved Seagull Optimization Algorithm

Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.

Long-Term Outcome of Cochlear Implantation in Children With Congenital, Perilingual, and Postlingual Single-Sided Deafness.

We investigated the long-term outcomes of children with single-sided deafness (SSD) after cochlear implant (CI) surgery, during and after rehabilitation, and compared the results of children with congenital, perilingual, and postlingual SSD. We evaluated the impact of SSD at age at onset and duration of deafness on their performance. Thirty-six children with SSD treated with CI participated in the study: 20 had congenital, seven perilingual (defined: >0 to 4 years), and nine had postlingual deafness (defined as >4 years of age). Their outcome with CI were measured on both subjective and objective scales: duration of device use, speech intelligibility in noise and in quiet, bilateral hearing and localization ability, quality of life and hearing, presence and loudness of tinnitus, and hearing ability of the better hearing ear. After a mean follow-up time of 4.75 years, 32 of the 36 children used their CI on a regular basis. The remaining four children were nonusers. These children had congenital SSD and were older than three years at the time of CI surgery. Overall, for congenital/perilingual and postlingual SSD, speech intelligibility in noise and the Speech, Spatial and Qualities of Hearing Scale (SSQ) speech subscore were significantly improved, as were their subjective and objective localization ability and hearing-related quality of life. Children with postlingual SSD benefited from the CI with regard to speech intelligibility, SSQ speech/spatial/total score, and localization error, and children with congenital SSD showed better results with a short duration of deafness of less than 3 years compared with those with a longer deafness period. Cochlear implantation is a successful treatment for children with congenital/perilingual or postlingual SSD. Results largely differed with respect to the onset and duration of deafness, and better outcomes were achieved by children with postlingual SSD and with a short duration of deafness. Our data also confirmed that children with congenital SSD should be implanted with a CI within three years of age.

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research. The source code is available at https://github.com/cszn/SCUNet.

Comparison of acoustic particle acceleration detection capabilities in three species of sharks.

Behavioural studies have shown that sharks are capable of directional orientation to sound. However, only one previous experiment addressed the physiological mechanisms of directional hearing in sharks. In this study we used a directional shaker table in combination with the auditory evoked potential (AEP) technique to understand the broadscale directional hearing capabilities in the New Zealand carpet shark Cephaloscyllium isabellum, rig shark Mustelus lenticulatus, and school shark Galeorhinus galeus. The aim of this experiment was to test if sharks are more sensitive to vertical (z-axis) or head-to-tail (x-axis) accelerations, and whether there are any differences between species. Our results support previous findings, suggesting that shark ears can receive sounds from all directions. Acceleration detection bandwidth was narrowest for the carpet shark (40-200Hz), and broader for rig and school sharks (40-800Hz). Greatest sensitivity bands were 40-80Hz for the carpet shark, 100-200Hz for the rig, and 80-100Hz for the school shark. Further, our results indicate that there may be differences in directional hearing abilities among sharks. The bottom-dwelling carpet shark is equally sensitive to vertical and head-to-tail particle accelerations. In contrast, both benthopelagic rig and school sharks appeared to be more sensitive to vertical accelerations at frequencies up to 200 Hz. This is the first study to provide physiological evidence that sharks may differ in their directional hearing and sound localisation abilities. Further comparative physiological and behavioural studies in more species with different lifestyles, habitats, and feeding-strategies are needed to further explore the drivers for increased sensitivity to vertical accelerations among elasmobranchs.

Multiobjective sparrow search feature selection with sparrow ranking and preference information and its applications for high-dimensional data

To reduce the dimensionality of high-dimensional data and enhance its classification accuracy, feature selection can be regarded as a multiobjective optimization problem that can be addressed by evolutionary computation algorithms with promising results. However, balancing the convergence and diversity of nondominated solutions remains challenging. To address these issues, this paper proposes a multiobjective sparrow search feature selection approach with sparrow ranking and preference information and its applications for high-dimensional data. First, during the population updating process, the updating formula of observers is combined with the mutualism phase of the symbiotic organisms search algorithm to design a location updating formula of observers in the sparrow search algorithm, balancing the local development ability and global search ability of sparrow search algorithm and increasing the possibility of moving closer to the optimal solution. Second, based on the dominant and nondominated sparrow individuals of each sparrow, the dominant and nondominated distances are defined, and the sparrow ranking is proposed. The feature ranking is then designed by using the definition of sparrow ranking. Following the definition of sparrow ranking and feature ranking, the selection strategy and location updating formula of the producers in the sparrow search algorithm are proposed. Finally, a preference information-based mutation algorithm is designed to augment the diversity of the nondominated solutions and more effectively guide the whole sparrow population to a better solution. The experimental results on 14 high-dimensional datasets show that the proposed algorithm outperforms different single-objective and multiobjective optimization feature selection algorithms in terms of classification efficiency and diversity of solutions.

A hybrid genetic tabu search algorithm for distributed flexible job shop scheduling problems

The distributed flexible job-shop scheduling problem (DFJSP) is an extension of the flexible job shop scheduling problem, which is a famous NP-complete combinatorial optimization problem. DFJSP consists of three sub-problems, assigning jobs to appropriate factories, scheduling operations to suitable machines, and determining the operation sequence on machines. Due to the complexity of DFJSP, there is only a little research on the problem. There are several benchmark sets proposed to test the performance of methods for solving DFJSP. However, many instances remain unresolved after a long time. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for DFJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. Two genetic operators are designed based on the critical factory to discrete the population. A new neighborhood structure is introduced into TS to search for more space of neighborhood solutions. To evaluate the performance of HGTSA, this paper compares it with four state-of-the-art algorithms on 69 benchmark instances. The experimental results demonstrate that HGTSA outperforms these comparison algorithms in terms of solution quality and computation efficiency. In particular, HGTSA has found 13 newly upper bounds of these benchmark instances.

Research on the Reconfiguration Method of Space-Based Exploration Satellite Constellations for Moving Target Tracking at Sea

In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate system, alongside the creation of a three-phase orbital manoeuvre model. A Non-dominant Sorting Adaptive Memetic (NSAM) algorithm is proposed in this paper, which is a two-layer multi-objective optimization algorithm that retains the advantages of evolutionary algorithms based on the population’s evolution and has an excellent local optimization ability of local search algorithms. The proposed algorithm can be used to solve multi-objective optimization problems. By comparing the target observation results before and after the satellite reconstruction simulation, it can be concluded that the orbital manoeuvring can effectively improve the observation probability and observation duration of the target at a certain speed. The orbital manoeuvre model created in this paper provides a certain methodical support for the tracking problem of moving targets at sea.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs.

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning-based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross-talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8mm, depth errors of 0.22mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter-element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system.

Elman short-term wind power prediction based on the optimized seagull algorithm

Accurate prediction of wind farm power output can relieve the pressure of grid frequency regulation and peak regulation and improve grid stability. With the goal of improving power prediction accuracy and reducing overall prediction error, this paper proposes an Elman short-term wind power prediction model on the basis of an optimized seagull algorithm. Firstly, the Elman network is used as the base prediction model, and the seagull algorithm is applied to seek the best values for its weights. Secondly, the chaotic circle mapping with better initial characteristics is improved to equalize its sequence distribution for optimizing the population initialization. Then, to address the lack of local search capability, an optimized iterative approach using the sine cosine operator is used to achieve a balance between local exploitation ability and global search capability. Finally, after simulation and analysis of the actual data set, it is verified that the model has a better prediction effect.

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model’s parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions.

Exploring of Minangkabau Local Knowledge Management in Agam District, Indonesia

Local knowledge is the knowledge found in local communities that consists of a set of experiences that have been accumulated and provides information that shows the local community or local culture’s behavioral characteristics. As a result of a variety of factors, such information is at risk of being obliterated, including lack of interest from younger generations and low life expectancy, where knowledge enables individuals to die before passing it to the next generation. Therefore, this paper aims to look at the perspective of community leaders on the status of local knowledge. The data collection method used in this research is an interview. The results of this study indicate that the Minangkabau community in Agam Regency, Indonesia recognises their customs, cultures, and habits that are different from the general public. The investigation was carried out by involving community leaders related to the dimensions of local knowledge: (1) The environmental dimension of local knowledge through Lubuak Larangan is a type of community custom and culture that involves the preservation of river and lake areas within specific territorial borders and rules; (2) dimensions of local values; (3) dimensions of local abilities are employed to ensure survival by farming, livestock or industry; (4) dimensions of local resources: utilization of natural resources. The Minangkabau community divided its forest into two categories: prohibited forest and forest cultivated or utilised for economic interests and family needs; (5) the local decision-making mechanism’s dimensions; and (6) local group solidarity dimensions: religious rituals, traditional ceremonies, arts, and cooperation.

Hybrid algorithm of differential evolution and flower pollination for global optimization problems

Differential evolution (DE) and flower pollination algorithm (FPA) are efficient and novel meta-heuristic algorithms, which are effective tools for solving global optimization problems (GOPs). However, a single algorithm is always restricted to its own principle and mechanism, and even suffers from the imbalance of global and local search ability when solving large-scale GOPs. The organic combination of the two algorithms with a certain strategy can improve the performance of the hybrid algorithm. Motivatedbythisidea, ahybridglobal optimizationalgorithm basedon DE and FPA is proposed. Firstly, an improved initial population generation method is providedforimproving the quality of the initial population. Secondly, an improved DE is given, and the parameters F and CR areadjustedin an adaptiveanddynamicfashion, and a combined mutation operator is designed. Thirdly, an improved FPA is proposed, and a method for adaptively and dynamically adjusting the switch probability p is given, and formulas for updating the global and local position are introduced. Lastly, a hybrid strategy of organically combining DE and FPA is proposed to form a hybrid algorithm of differential evolution and flower pollination (HADEFP). To demonstrate the performance of the hybrid algorithm, the proposed hybrid algorithm is tested against other algorithms in the literature on CEC 2017 test functions (10-D, 50-D and 100-D) and practical optimization problem, respectively. The results show that the proposed HADEFP outperforms other comparison algorithms and has better robustness.

Advanced strategies on update mechanism of tree-seed algorithm for function optimization and engineering design problems

Tree-seed algorithm (TSA) is one of the meta-heuristic algorithms proposed recently. It has a simple but effective structure. However, for complex high-dimensional functions and constrained optimization problems, TSA has the defects of slow convergence rate and low optimization accuracy. To overcome these issues, an improved tree-seed algorithm (SATSA) based on Nelder-Mead simplex method is proposed. Firstly, appropriately increase the value of the control parameters that balance exploration and exploitation, thereby improving the local search ability of TSA, and introduce an adaptive stochastic component of Gaussian distribution in the seed generation formula of the local search stage. Then, set the maximum retention times of each tree, for trees that exceed the maximum retention times in continuous iterations, increase the number of seeds produced by this tree, then use a new equation based on the global optimal location to generate seeds, thereby speeding up the convergence of the algorithm. Finally, after all trees produce seeds, introduce the improvement strategy with Nelder-Mead simplex method into some poor quality solutions, so that the solution with poor quality is searched deeply in the neighborhood, speeding up the process of moving the overall solution to the optimal solution. Its convergence is proved by theoretical analysis, and certifies that the time complexity of SATSA is not increased compared to TSA. The simulation results of 6 advanced algorithms on CEC2017 test suite and 10 challenging engineering constrained optimization problems demonstrate that the three strategies proposed in this study are feasibility and effectiveness.

Perforated Concave Earplug (pCEP): A Proof-of-Concept Earplug to Improve Sound Localization without Compromising Noise Attenuation.

Combat soldiers are currently faced with using a hearing-protection device (HPD) at the cost of adequately detecting critical signals impacting mission success. The current study tested the performance of the Perforated-Concave-Earplug (pCEP), a proof-of-concept passive HPD consisting of a concave bowl-like rigid structure attached to a commercial roll-down earplug, designed to improve sound localization with minimal compromising of noise attenuation. Primarily intended for combat/military training settings, our aim was an evaluation of localization of relevant sound sources (single/multiple gunfire, continuous noise, spoken word) compared to 3M™-Combat-Arms™4.1 earplugs in open-mode and 3M™-E-A-R™-Classic™ earplugs. Ninety normal-hearing participants, aged 20-35 years, were asked to localize stimuli delivered from monitors evenly distributed around them in no-HPD and with-HPD conditions. The results showed (1) localization abilities worsened using HPDs; (2) the spoken word was localized less accurately than other stimuli; (3) mean root mean square errors (RMSEs) were largest for stimuli emanating from rear monitors; and (4) localization abilities corresponded to HPD attenuation levels (largest attenuation and mean RMSE: 3M™-E-A-R™-Classic™; smallest attenuation and mean RMSE: 3M™-Combat-Arms™4.1; pCEP was mid-range on both). These findings suggest that the pCEP may benefit in military settings by providing improved sound localization relative to 3M™ E-A-R™-Classic™ and higher attenuation relative to 3M™-Combat Arms™-4.1, recommending its use in noisy environments.

Chaotic heterogeneous comprehensive learning PSO method for size and shape optimization of structures

This paper proposes a novel chaotic heterogeneous comprehensive learning particle swarm optimization (CLPSO) method for the simultaneous size and shape design of structures. The heterogeneous CLPSO divides the particles into two explorative and exploitative subpopulations. The exploration performs the global searches for the set of best particles experienced solely within its own subpopulation, whilst the exploitation refines the deep searches learnt from the global best particle over an entire population. In essence, the proposed method maintains a good balance between the global explorative and local exploitative optimization schemes. The global searches within the explorative subpopulation are independent to the exploitative simulations even if the latter scheme prematurely converges to the local swarm position. For both subpopulations, the comprehensive learning approach constructs the particles through a cross-positioning process on the individual variable space and avoids the local optimal pitfall. Moreover, the chaotic logistic map within the exploitative optimization tests the global best particle through the set of diversely generated samples and hence enhances the local search ability. Various enriching techniques, including automatic adaptive (inertial weight and acceleration) parameters with dynamic space reduction, are incorporated to improve the likelihood of finding the optimal solution of practical-scale problems at modest computing efforts. The accuracy and robustness of the proposed method are illustrated through a number of planar and spatial truss design benchmarks subjected to the challenging nonconvex and/or nonsmooth programs.

Water grade judgment of aquaculture based on IBES and evidence fusion

In aquaculture, water measuring data obtained by conventional multi-sensors often have some errors, therefore, a new water grade judgment method is proposed in the paper. First, by using the refractive learning strategy, an improved Bald Eagle Search algorithm (IBES) is studied. IBES can effectively overcome the problem of weak local search ability and slow convergence speed in traditional BES algorithm. Second, an aquaculture water grade judgment method based on IBES and evidence fusion is proposed. The interval number is used to represent the aquaculture water parameters, and the reliability coefficient Nk is optimized by IBES to complete the modification of the mass function. Then, the combination rule of interval evidence and the modified mass function are synthesized to obtain the comprehensive interval evidence. Finally, the water grade is judged according to the decision rule. In the experiments, three sensors (temperature, dissolved oxygen and pH) are used to obtain water data. The experimental results show that the median value of the fusion uncertainty obtained by the proposed method is reduced to 0.3769, its reliability is greatly improved compared with a single sensor. In addition, the performance of the proposed method is compared with other optimization algorithms, including GWO, ABC, PSO, WOA, BES, IBES, the experimental results show that the average probabilities of IBES in judgment of water grades for Excellent (I) and Poor (III) are all 93% which rank first. Therefore, the method can accurately determine the water grade from uncertain measurement data with inevitable error and random error, it can provide a new idea for aquaculture water grade judgment.

PSO with improved local unimodal search ability for incipient pump fault identification

PSO with improved local unimodal search ability for incipient pump fault identification

Research on Unmanned Aerial Vehicle Cluster Collaborative Countermeasures Based on Dynamic Non-Zero-Sum Game under Asymmetric and Uncertain Information

Unmanned aerial vehicle (UAV) swarm coordinated confrontation is a hot topic in academic research at home and abroad, and dynamic maneuver decision-making is one of the most important research fields for UAV countermeasures. Aiming at the complexity, uncertainty and confrontation of UAV cooperative confrontation, concepts such as relative advantage degree and advantage coefficient are introduced, and game theory is used as a framework to construct a dynamic non-zero-sum game UAV cluster cooperative confrontation decision-making model, and finally convert it into an optimization problem. On this basis, using the Nash equilibrium solution method of multi-strategy fusion particle swarm algorithm, by introducing adaptive inertia weight and local mutation strategy, while enhancing the diversity of the population, it can ensure the local accurate search ability of the particle swarm. The simulation results of the example are verified. The effectiveness of the proposed model and method is confirmed.