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English is a universal language and is widely used. In order to deepen the cognition of language, this paper proposes to analyse the variation of metadiscourse verb patterns in English academic papers from within and between disciplines. By comparing the metadiscourse chunks used in various disciplines, it is concluded that there are obvious differences in the verb types of the subject papers. Analysing various identification algorithms of verb types, it is obtained that the combination of the word movement distance (WMD) model and the R&L density peak clustering algorithm is the best. Using the R&L density peak clustering, identification parameters are easy to determine, and when combined with the WMD model to calculate the similarity of words, it improve the accuracy of verb pattern clustering. By comparison, it is proved that the accuracy of the algorithm combining the WMD model and R&L density peak clustering reaches 30.06%, and the effect of identifying verb pattern variation is the best.

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed. Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios. Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks. [ABSTRACT FROM AUTHOR]

Digital geological survey methods have become supplementary approaches for traditional geological survey in the last two decades. In this paper, the Unmanned Aerial Vehicle (UAV)‐based photogrammetry technology is used to obtain the 3D point cloud model of rock outcrops. The clustering algorithm is used to automatically identify the rock discontinuity parameters. However, the obtained 3D point cloud model with high resolution often has a huge point data which usually poses a great challenge to the computational efficiency of the automatic identification. In fact, too‐dense point cloud data may not be necessary for cases when the rock mass is relatively intact. The optimal point cloud resolution, which balances the accuracy and efficiency, depends on the degrees of fragmentation of the rock mass under investigation. For a model with the same resolution, large‐size discontinuities may have quite a few redundant point cloud data that are of little use to improve the identification accuracy whereas small‐size discontinuities may not be properly identified due to insufficient number of data points. In this paper, the influence of the degree of fragmentation of rock mass on the identified results was investigated. The uniform grid method was adopted to sparse the 3D point cloud model. The optimal point cloud resolution for different discontinuities was suggested. The applicability and feasibility of the proposed approach were verified via three illustrative examples of typical rock slopes. [ABSTRACT FROM AUTHOR]

Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flocks. Traditional clustering algorithms need to set various prior parameters, including the number of groups, the number of nearest neighbors, or the minimum number of individuals. However, flocks may display complex group behaviors (splitting, combination, etc.), which complicate the choice and adjustment of the parameters. This paper uses a real-time clustering-based method that utilizes concepts from the algebraic graph theory. The connected graph is used to describe the spatial relationship between the targets. The similarity matrix is calculated, and the problem of group clustering is equivalent to the extraction of the partitioned matrices within. This method needs only one prior parameter (the similarity distance) and is adaptive to the group's splitting and combination. Two modifications are proposed to reduce the computation burden. First, the similarity distance can be broadened to reduce the exponent of the similarity matrix. Second, the omni-directional measurements are divided into multiple sectors to reduce the dimension of the similarity matrix. Finally, the effectiveness of the proposed method is verified using the experimental results using real radar data. [ABSTRACT FROM AUTHOR]

This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies (RSs). The proposed model is composed of two states. In the first state, decision tree, random forest, gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost) models are used to investigate the importance of features. The features that have a high influence on RSs are first selected. In the second state, a K-means clustering method is used to uncover the interdependences between RSs and the influencing features, based on the results in the first state. The proposed method can determine the quantitative relationships between RSs and influencing factors. The results clearly show the influences of the factors on RSs, the possibilities of different train operation RSs under different situations, as well as some key time periods and key trains that the controllers should pay more attention to. The research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs. [ABSTRACT FROM AUTHOR]

Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Rural house is a fundamental component of rural settlements, and understanding its construction and development characteristics is crucial for rural land use and development planning. This paper focuses on the spatiotemporal characteristics and influencing factors of Rural Houses Construction Development (RHCD) from 1980 to 2019 with a case study of Qingyuan Town in China. Based on the literature review and filed research, a set of evaluation indicators for RHCD was established. The article calculates RHCD indicators from temporal and spatial dimensions, uses the location entropy method to demonstrate the spatial distribution of indicators, and classifies the RHCD type of 14 villages in Qingyuan Town using clustering algorithms. It also analyzes the influencing factors of spatiotemporal distribution. The results show that the RHCD in Qingyuan Town exhibits typical characteristics of mountainous areas and aligns with the development trends of rural society in China. Population growth, geographical location, and economic development are the primary driving factors for the quantity indicator (Qi), while economic growth, construction technology, industrial development, and policy adjustments are the key factors influencing the form indicator (Fi). In future policy-making, greater emphasis should be placed on optimizing development strategies, improving data and monitoring systems, and integrating administrative strength with actual development needs. [ABSTRACT FROM AUTHOR]

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence. [ABSTRACT FROM AUTHOR]

Background: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. Methods: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. Results: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. Conclusions: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks. [ABSTRACT FROM AUTHOR]

With the spread of electronic toll collection (ETC) and electronic payment, it is still a challenging issue to develop a systematic approach to investigate highway travel patterns. This paper proposed to explore spatial–temporal travel patterns to support traffic management. Travel patterns were extracted from the highway transaction dataset, which provides a wealth of individual information. Additionally, this paper constructed the analysis framework, involving individual, and temporal and spatial attributes, on the basis of the RFM (Recency, Frequency, Monetary) model. In addition to the traditional factors, the weekday trip and repeated rate were introduced in the study. Subsequently, various models, involving K-means, Fuzzy C-means and SOM (Self-organizing Map) models, were employed to investigate travel patterns. According to the performance evaluation, the SOM model presented better performance and was utilized in the final analysis. The results indicated that six groups were categorized with a significant difference. Through further investigation, we found that the random traveler occupied over 40% of the samples, while the commuting traveler and long-range freight traveler presented relatively fixed spatial and temporal patterns. The results were also meaningful for highway authority management. The discussion and implication of travel patterns to be integrated with the dynamic pricing strategy were also discussed. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, communities utilize unmanned vehicles to minimize person-to-person contact and lower the risk of infection. This paper addresses the critical considerations of these unmanned vehicles' maximum load capacity and service time, formulating them as constraints within a multi-traveling salesman problem (MTSP). We propose a comprehensive optimization approach that combines a genetic simulated annealing algorithm with clustering techniques and an improved Hopfield neural network (IHNN). First, the MTSP is decomposed into multiple independent TSPs using the fuzzy C-means clustering algorithm based on a genetic simulated annealing algorithm (SA-GA-FCM). Subsequently, the HNN is employed to introduce the data transformation technique and dynamic step factor to prepare more suitable inputs for the HNN training process to avoid the energy function from falling into local solutions, and the simulated annealing algorithm is introduced to solve multiple TSP separately. Finally, the effectiveness of the proposed algorithm is verified by small-scale and large-scale instances, and the results clearly demonstrate that each unmanned vehicle can meet the specified constraints and successfully complete all delivery tasks. Furthermore, to gauge the performance of our algorithm, we conducted ten simulation comparisons with other combinatorial optimization and heuristic algorithms. These comparisons indicate that IHNN outperforms the algorithms mentioned above regarding solution quality and efficiency and exhibits robustness against falling into local solutions. As presented in this paper, the solution to the unmanned vehicle traveling salesman problem facilitates contactless material distribution, reducing time and resource wastage while enhancing the efficiency of unmanned vehicle operations, which has profound implications for promoting low-carbon sustainable development, optimizing logistics efficiency, and mitigating the risk of pandemic spread. [ABSTRACT FROM AUTHOR]

Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some extent. This paper proposes a raster-based DWT (discrete wavelet transform) method for thunderstorm identification, this method uses DWT, CFSFD (clustering algorithm for fast search and finding density peaks) algorithm and ADTD (active divectory topology diagrammer) lightning location data for thunderstorm identification. The advantage of this method is that it supports different spatial resolutions and can identify any shape and number of thunderstorms at the same time and in the same area. It is effective in eliminating some of cluttered, scattered lightning data and extracting dense areas of thunderstorms. Furthermore, the method has a time complexity of O(n), and the computational efficiency is significantly better than the current TITAN (thunderstorm identification, tracking, analysis, and nowcasting) algorithm, which provides a good basis for subsequent extrapolation studies of thunderstorms. [ABSTRACT FROM AUTHOR]

Clustering research is an important field in machine learning, pattern recognition and other fields. The neutrosophic set characterizes the data through true membership functions, indeterminate membership functions and false membership functions. Data clustering using neutrosophic set has become one of the current research hotspots. In this paper, first, a new definition of data uncertainty in a neutrosophic set is proposed in this paper based on the density of data. Next, a clustering model based on the uncertainty value of neutrosophic set data is proposed by considering the main cluster (true membership) and the noise cluster (false membership) in the data set. The model takes into account the distance of the data points to the cluster centers and the indeterminacy value of each data point, and then minimizes the proposed cost function by the method of Lagrangian multipliers. The true membership value and false membership value of each data point can be obtained. Finally, the effectiveness of the method is demonstrated by experiments on the various datasets. Experimental results show that the cost function has more accurate membership degree when dealing with boundary points and outliers, and outperforms existing clustering methods on datasets. [ABSTRACT FROM AUTHOR]

With the accelerated digital transformation, food security data is exponentially growing, making it difficult to process and analyze data as the primary challenge for food security risk regulation. The promotion of "big data + food" safety supervision can effectively reduce supervision costs and improve the efficiency of risk detection and response. In order to improve the utilization of testing data and achieve rapid risk assessment, this paper proposes a rice security risk assessment method based on the fusion of multiple machine learning models, and conducts experimental validation based on rice hazard detection data from 31 provinces in China excluding Hong Kong, Macao and Taiwan in 2018. The model comparison verifies that the risk assessment model shows better performance than other mainstream machine learning algorithms, and its evaluation accuracy is as high as 99.54%, which verifies that the model proposed in this paper is more stable and accurate, and can provide accurate and efficient decision-making basis for regulatory authorities. [ABSTRACT FROM AUTHOR]

Long-range surveillance and early warning of space targets are significant factors in space security. Under remote observation conditions, the energy performance of the target is weak and subject to environmental and imaging process contamination. Most detection methods are aimed at targets with a high signal-to-noise ratio (SNR) or local contrast, and the detection performance for dim-weak small targets is poor; therefore, the target signal is often enhanced by energy accumulation. However, owing to the interference caused by the instability of the imaging system, energy accumulation errors occur in the target, resulting in the dispersion of the target energy, making detection a challenge. To solve the above problem, this study proposed a multi-frame superposition detection method for dim-weak point targets based on an optimized clustering algorithm by combining the clustering method with the inherent features of the target and using the difference between the target and noise energy distribution for detection. First, we simulated the multi-frame imaging process of the target post-disturbance and established an optical imaging system model of the dim-weak target. Subsequently, we used data dimension reduction and outlier removal to extract the target potential area. Finally, the data were sent to the clustering model for calculation and judgment. Given that the accuracy rate reaches 87.1% when the SNR is 1 dB, the experimental results show that the detection method proposed in this paper can effectively detect dim-weak targets with low SNR. In addition, there is a significant improvement in the detection performance of the receiver characteristic curve compared with other algorithms in the real scene, which further proves the superiority of the method in this paper. [ABSTRACT FROM AUTHOR]

The increasing proportion of night consumption in the total daily consumption means the night economy has become an indispensable part of national economic development. However, early social science research on alcohol in the night economy and social science research on the night leisure industry dominated our understanding of the night city. Few researchers have built effective mathematical statistical models to explore the spatio-temporal distribution and regional interactions of the night economy. This paper presents a method to analyze the spatial and temporal distribution pattern of the night economy based on multi-source data. Firstly, K-Means++ and DBSCAN were used to cluster OD points to identify the gathering areas of night activities. Then, the local L-function in "flow space" was used to extract the aggregated flow of each aggregation area and analyze the regional interactions. Finally, the correlation between night activity and night service facilities was calculated by using geographic detector, and night activity and lighting were coupled by the profit and loss value. The research shows that this method can identify the main areas of night activity, dig out the interrelationships at the community level, and find the new night activity gathering points and night economic growth areas in the future. This study extends the current situation that the night economy is limited to theoretical research and research in central urban areas, provides the temporal and spatial distribution of night activities and night lighting supplies from the perspective of big data, and provides a basis for future night economy research, urban planning, and relevant policy issuance. [ABSTRACT FROM AUTHOR]

On the basis of analyzing the problems concerning hotel accommodation recommendation (HAR), this paper constructs a tourism HAR algorithm based on the CS-IDIANA clustering model (cellular space-improved divisive analysis). The algorithm integrates the cellular space model with DIANA, and takes the tourist attractions and the travel route costs as the research background and constraint conditions. Considering the feature attributes and spatial attributes of the tourist attractions, the tourist attraction recommendation algorithm based on the CS-IDIANA clustering model is established, then the HAR algorithm based on the spatial accessibility and route cost is constructed, with the constraints of the spatial accessibility field strength (SAFS) between the hotels and attractions and the travel route costs between the hotels and attractions. The experiment selects the tourism city Zhengzhou as the research object, and the experimental results are analyzed in four dimensions: the clustering results, the recommendation field strength of the tourist attractions, the hotel SAFS and the HAR results. The experiment proves that the proposed algorithm can find the best matched tourist attractions for tourists and the hotels with the lowest tour route cost based on the constraint conditions. Compared to the suboptimal hotels, the route costs are reduced by 5.67% and 9.63%, respectively. Compared to the hotel with the highest route cost, it reduces the travel costs by 29.23%. Compared with the two commonly used recommendation methods, the UCFR (user-based collaborative filtering recommendation) and ICFR (item-based collaborative filtering recommendation), the proposed CSIDR (CS-IDIANA recommendation) has a higher accuracy and recall rate. [ABSTRACT FROM AUTHOR]

The maintenance strategies commonly employed in industrial settings primarily rely on theoretical models that often overlook the actual operating conditions. To address this limitation, the present paper introduces a novel selective predictive maintenance approach based on a machine learning model for a multi-parallel series system, which involves executing multiple missions with breaks between them. For this purpose, the proposed selective maintenance approach consists of finding, at each breakdown, the optimal structure of maintenance activities that provide the desired reliability level of the system for each mission. This decision is based on a component's actual age, as determined by the prediction model. In addition, an optimization model with the Extended Great Deluge (EGD) algorithm uses these predictions as input data to identify the best maintenance level for each component considering the constrained maintenance resources. Finally, the numerical results of the proposed idea applied to the Flexible Manufacturing System (FMS) data are presented to show the robustness of the model. [ABSTRACT FROM AUTHOR]

This paper discusses the innovative application of a new methodology for acquiring load curves in energy systems such as Azogues Electric Company (EEA). The proposed approach imports the database from Excel and, through an iterative clustering algorithm based on density with noise, generates daily load curves with a breakdown of weekdays and weekends, identifying one curve by type of consumer. Moreover, the groups obtained are validated by means of a Silhouette verification index (IS) identifying bad groupings, which are discarded to obtain results. Per unit value responses are presented through tables with hourly values on weekdays and weekends. The graphical comparison with the previous methodology of real measurements in Excel is also added. [ABSTRACT FROM AUTHOR]

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform. [ABSTRACT FROM AUTHOR]

The precision with which directional overcurrent relays (DOCRs) are set up establishes the microgrid customers' access to reliable and uninterrupted electricity. In order to avoid failure in DOCRs operation, it is critical to consider a single contingency (N-1 event) on the protection optimization setting problem (POSP). However, power systems may face cascading outages or simultaneous contingencies (N-K events), which greatly expand the problem's complexity and scale. The effect of cascading events on this problem is an open research gap. Initially, this paper proposes a novel approach to reducing the scale of simultaneous events called the N-K events scale reduction technique (N-K-ESRT). Moreover, an innovative method named fuzzy zero-violation clustering is utilized to group these contingencies. Ultimately, the DOCRs' decision parameters are generated by three optimization algorithms, namely interior point (IPA), simulated annealing, and pattern search. In all case studies (including a real industrial network called TESKO2 feeder, the IEEE Std. 399-1997, and the IEEE 14 bus systems), the capabilities of the proposed method are effectively validated based on the DOCR's tripping time and the algorithm's execution time. [ABSTRACT FROM AUTHOR]

With the rapid increase in new energy penetration, the uncertainty of the power system increases sharply. We can smooth out fluctuations and promote the more grid-friendly integration of new energy by combining it with energy storage. This paper proposes an evaluation method for assessing the value of a combined power plant system of new energy and energy storage using robust scheduling rules. Firstly, the k-means clustering algorithm is improved by using the elbow method in order to generate typical scenarios that can be used for the operation optimization of the combined power plant system of new energy and energy storage. Then, a two-stage robust optimization model of the combined power plant system of new energy and energy storage with a min–max–min structure is constructed according to the uncertainty of new energy. In this model, the operation constraints and coordinated control of wind–solar–thermal–storage units are considered. By constructing the uncertainty set of the new energy output, the overall operating cost of the system is minimized and uncertainty adjustment parameters are introduced to flexibly adjust the conservatism of the scheduling rules. Furthermore, based on the column and constraint generation algorithm and strong duality theory, the original problem can be decomposed into a master problem and subproblems with mixed integer linear characteristics for an alternating solution, so as to obtain the optimal solution of the original problem, and finally obtain the robust scheduling rule with the lowest operating cost under the worst scenario. Finally, based on the wind and solar power output curves and the output of each unit under the robust scheduling rules, combined with the value estimation method of the combined power plant system of new energy and energy storage, the value of the combined power plant system of new energy and energy storage is evaluated. Through the establishment of models and example analysis, it is proven that raising the quantity of the grid-connected power generated with new energy will cause an increase in the volatility of the power system; it will also bring considerable benefits to new energy plants, and the energy storage can improve the stability of the system. The above can provide references for the subsequent energy storage configuration in the planning of a combined power plant system of new energy and energy storage. [ABSTRACT FROM AUTHOR]

Plant diseases and pests may seriously affect the yield of crops and even threaten the survival of human beings. The characteristics of plant diseases and insect pests are mainly reflected in the occurrence of lesions on crop leaves. Machine vision disease detection is of great significance for the early detection and prevention of plant diseases and insect pests. A fast detection method for lesions based on a single-channel gravitational kernel density clustering algorithm was designed to examine the complexity and ambiguity of diseased leaf images. Firstly, a polynomial was used to fit the R-channel feature histogram curve of a diseased leaf image in the RGB color space, and then the peak point and peak area of the fitted feature histogram curve were determined according to the derivative attribute. Secondly, the cluster numbers and the initial cluster center of the diseased leaf images were determined according to the peak area and peak point. Thirdly, according to the clustering center of the preliminarily determined diseased leaf images, the single-channel gravity kernel density clustering algorithm in this paper was used to achieve the rapid segmentation of the diseased leaf lesions. Finally, the experimental results showed that our method could segment the lesions quickly and accurately. [ABSTRACT FROM AUTHOR]

Production profiling technology is an important method for monitoring the dynamics of oil and gas reservoirs which can effectively improve the efficiency of oil recovery. Production profiling is a technique in which a test instrument is lowered from the tubing to the bottom of the well to measure flow, temperature, pressure, and density in a multi-layer section of a producing well. Normal production profiling process needs to stop production, operate complex, consume long time and high cost. Furthermore, the profile cannot be continuously monitored for a long time. To address these limitations, this paper proposes a production profiling interpretation method based on reservoir primitive microbial DNA sequencing. The microbial stratigraphic baseline with high-resolution features is obtained by sampling and DNA sequencing of produced fluid and cuttings from different wells. Specifically, the random forest algorithm is preferred and improved by comparing the accuracy, precision, recall, F1-score, and running time of three clustering methods: Naïve-Bayes classifier, random forest classifier, and back-propagation classifier. Constructing PSO-random forest model is based on stratigraphic records and produced fluid bacteria features. The computational accuracy and efficiency of this method allows it to describe the production profile for each formation. Moreover, this test process does not need to stop production with simple operation and does not pollute the formation. Meanwhile, by sampling fluid production at different stages, it can achieve the purpose of long-term effective dynamic monitoring of the reservoir. [ABSTRACT FROM AUTHOR]

To address the problem of low overall machining efficiency of free-form surfaces and difficulty in ensuring machining quality, this paper proposes a MATLAB-based free-form surface division method. The surface division is divided into two stages: Partition area identification and area boundary determination. In the first stage, the free-form surface is roughly divided into convex, concave, and saddle regions according to the curvature of the surface, and then the regions are subdivided based on the fuzzy c-means clustering algorithm. In the second stage, according to the clustering results, the Voronoi diagram algorithm is used to finally determine the boundary of the surface patch. We used NURBS to describe free-form surfaces and edit a set of MATLAB programs to realize the division of surfaces. The proposed method can easily and quickly divide the surface area, and the simulation results show that the proposed method can shorten machining time by 36% compared with the traditional machining method. It is proved that the method is practical and can effectively improve the machining efficiency and quality of complex surfaces. [ABSTRACT FROM AUTHOR]

To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets. Nevertheless, the rapid growth of the number of states in a TERG makes the problem intractable for large systems. In this paper, we improve the existing clustering TERG approach, and we make it suitable for large sized batch processes. We also enlarge a systematic approach to model batch processes with timed Petri nets. Finally, a comprehensive example of scheduling problem is studied for an archetypal chemical production plant in order to illustrate the efficiency of the proposed approach. • The scheduling issue of batch chemical processes is formalized with timed Petri nets. • A graph named ClusTERG approximates the state space through hierarchical clustering. • The approximation error of this ClusTERG is far less than other approximations. • Such an approach is used to obtain near-optimal schedules of a batch process. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things.This paper proposes an efficient intrusion detection method for applications with Internet of Things. [ABSTRACT FROM AUTHOR]

Composite insulations, such as ceramics, are commonly utilized in the turbine system as a thermal coating barrier to protect the metal substrate against high temperatures and pressure. The presence of delamination in the composite insulations may cause turbine failure, leading to a catastrophic accident. Thus, regular non-destructive testing is required to detect and evaluate insulation defects. Among the non-destructive testing techniques, the microwave technique has emerged as a promising method for assessing defects in ceramic coatings. Although the method is promising, microwave non-destructive testing suffers from poor spatial imaging, making the defect assessment challenging. In this paper, a novel technique based on microwave non-destructive testing with a k-medoids clustering algorithm for delamination detection is proposed. The representative ceramic coating sample is scanned using a Q-band open-ended rectangular waveguide with 101 frequency points that operated between 33 to 50 GHz. The measured data is transformed from the frequency domain to the time domain using an inverse fast Fourier transform. The principal component analysis is then used to reduce the dimensionality of 101 time steps into only 3 dominant attributes. The attributes of each inspected location are classified as defect or defect-free using the k-medoids clustering algorithm for accurately detecting and sizing the defects in the ceramic insulation. The results reported in this paper highlight the superiority of the k-medoids clustering algorithm in delamination detection, with an accuracy rate of 95.4%. This is a significant step forward compared to earlier approaches for identifying ceramic defects. [ABSTRACT FROM AUTHOR]

There are various types of autonomous unmanned systems, covering different spaces of sea, land, and air, and they are comprehensively going deep into multiple fields of national security and social life. Due to the development of technology, the scale of unmanned systems is getting larger and larger, the number of components in the system is increasing, and the operating environment of the system is also becoming more and more complex. Therefore, the probability of failure of the components of the system will also be significantly increased. In order to eliminate the impact of the fault in time, the fault diagnosis method is significant. Considering the differences of components in autonomous unmanned systems, if a specific fault diagnosis algorithm is designed for each type of component, it will bring difficulties to the coordinated control of the system. Therefore, this paper analyzes the data characteristics of unmanned autonomous system devices (such as sensors) and finds that these data have time series. Therefore, the data of different devices can be converted into time series, and a general fault diagnosis algorithm suitable for most devices can be studied. The fault diagnosis algorithm is based on the clustering algorithm. In order to improve the clustering effect, the time series of different devices are represented by Gaussian mixture clustering to reduce the computational complexity of the clustering calculation. Then, a time series similarity measurement method based on the improved Markov chain is proposed. This method can better distinguish normal samples from abnormal samples so as to classify and identify faults effectively. [ABSTRACT FROM AUTHOR]

The energy transition for decarbonization requires consumers' and producers' active participation to give the power system the necessary flexibility to manage intermittency and non-programmability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non-existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO). [ABSTRACT FROM AUTHOR]

Environment perception is the foundation of the intelligent driving system and is a prerequisite for achieving path planning and vehicle control. Among them, obstacle detection is the key to environment perception. In order to solve the problems of difficult-to-distinguish adjacent obstacles and easy-to-split distant obstacles in the traditional obstacle detection algorithm, this study firstly designed a 3D point cloud data filtering algorithm, completed the point cloud data removal of vehicle body points and noise points, and designed the point cloud down-sampling method. Then a ground segmentation method based on the Ray Ground Filter algorithm was designed to solve the under-segmentation problem in ground segmentation, while ensuring real time. Furthermore, an improved DBSCAN (Density-Based Spatial Clustering of Application with Noise) clustering algorithm was proposed, and the L-shaped fitting method was used to complete the 3D bounding box fitting of the point cloud, thus solving the problems that it is difficult to distinguish adjacent obstacles at close distances caused by the fixed parameter thresholds and it is easy for obstacles at long distances to split into multiple obstacles; thus, the real-time performance of the algorithm was improved. Finally, a real vehicle test was conducted, and the test results show that the proposed obstacle detection algorithm in this paper has improved the accuracy by 6.1% and the real-time performance by 13.2% compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

The advantages of FCM algorithm are that it is mainly applied in point data cluster and cannot directly process relational data, for which the paper proposes a clustering algorithm in data mining based on web log. Firstly, the paper improves FCM algorithm which makes it process relational data, and makes robustness improvement on the algorithm. Then, the traditional FCM algorithm needs to determine in advance on the basis of no prior knowledge, for which the paper introduces competition agglomerative algorithm and makes it combine with FCM algorithm, which generates CA-FCM algorithm making it automatically determine category number of the best classification. The experiments show that mining results of CA-FCM algorithm is close to the mining results of FCM algorithm, and the performance of CA-FCM algorithm is better than that of FCM algorithm when the number of users access to session is not large. [ABSTRACT FROM AUTHOR]

Under the imbalanced dataset, the performance of the base-classifier, the computing method of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem to improve the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on undersampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K-means clustering algorithm to under-sample the majority class samples and obtain K cluster centers. Then, all cluster centers (or the sample of the nearest cluster center) are regarded as new majority samples to construct a new balanced training subset combine with the minority class's samples. Repeat those processes to generate multiple training subsets and get multiple base-classifiers. However, with the increasing of iterations, the number of base-classifiers increase, and the similarity among the base-classifiers will also increase. Therefore, it is necessary to select some baseclassifier with good classification performance and large difference for ensemble. In the stage of selecting base-classifiers, according to the difference and performance of base-classifiers, we use the idea of maximum correlation and minimum redundancy to select base-classifiers. In the ensemble stage, G-mean or F-mean is selected to evaluate the classification performance of base-classifier for imbalanced dataset. That is to say, it is selected to compute the weight of each base-classifier. And then the weighted voting method is used for ensemble. Finally, the simulation results on the artificial dataset, UCI dataset and KDDCUP dataset show that the algorithm has good generalization performance on imbalanced dataset, especially on the dataset with high imbalance degree. [ABSTRACT FROM AUTHOR]

Gate-level circuit partitioning is an important development trend for improving the efficiency of simulation in EDA software. In this paper, a gate-level circuit partitioning algorithm, based on clustering and an improved genetic algorithm, is proposed for the gate-level simulation task. First, a clustering algorithm based on betweenness centrality is proposed to quickly identify clusters in the original circuit and achieve the circuit coarse. Next, a constraint-based genetic algorithm is proposed which provides absolute and probabilistic genetic strategies for clustered circuits and other circuits, respectively. This new genetic strategy guarantees the integrity of clusters and is effective for realizing the fine partitioning of gate-level circuits. The experimental results using 12 ISCAS '89 and ISCAS '85 benchmark circuits show that the proposed algorithm is 5% better than Metis, 80% better than KL, and 61% better than traditional genetic algorithms for finding the minimum number of connections between subsets. [ABSTRACT FROM AUTHOR]

In this paper, the novel study of an Internet of Things (IoT) network model with multimodal node distribution and a data-collecting mechanism using mobile clustering nodes is presented. The aim of this work is to introduce the problem of organizing the mobile cluster head IoT network with a heterogeneous distribution node in the service area with multimodal distribution nodes. A new method for clustering a heterogeneous network is proposed, which makes it possible to efficiently identify clusters that differ in terms of the density of nodes. This makes it possible to choose the speed of the mobile cluster head in accordance with the density in each cluster. The proposed method uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. One of the benefits of our proposed model is the increase in the efficiency of using a mobile cluster head. The new solution can be used to organize data collection in the IoT. [ABSTRACT FROM AUTHOR]

Quantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation, the machine learning models are continuously evolving. Hence, the new challenge is to improve the existing complex and critical machine learning training models. Therefore, the healthcare sector is shifting from a classical to a quantum domain to sustain patient-oriented attention to healthcare patrons. This paper presents a hybrid classical-quantum approach for training the unsupervised data models. In order to achieve good performance and optimization of the machine learning algorithms, a quantum k-means (QK-means) clustering problem was deployed on the IBM quantum simulators, i.e.,the IBM QASM simulator. In the first place, the approach was theoretically studied and then implemented to analyze the experimental results. The approach was further tested using small synthetics and cardiovascular datasets on a qsam simulator to obtain the clustering solution. The future direction connecting the dots is the incremental k-means algorithm with the quantum platform, which would open hitherto unimaginable technological doors. [ABSTRACT FROM AUTHOR]

Fashion image clustering is the key to fashion retrieval, forecasting, and recommendation applications. Manual labeling-based clustering is both time-consuming and less accurate. Currently, popular methods for extracting features from data use deep learning techniques, such as a Convolutional Neural Network (CNN). These methods can generate high-dimensional feature vectors, which are effective for image clustering. However, high dimensions can lead to the curse of dimensionality, which makes subsequent clustering difficult. The fashion images-oriented deep clustering method (FIDC) is proposed in this paper. This method uses CNN to generate a 4096-dimensional feature vector for each fashion image through migration learning, then performs dimensionality reduction through a deep-stacked auto-encoder model, and finally performs clustering on these low-dimensional vectors. High-dimensional vectors can represent images, and dimensionality reduction avoids the curse of dimensionality during clustering tasks. A particular point in the method is the joint learning and optimization of the dimensionality reduction process and the clustering task. The optimization process is performed using two algorithms: back-propagation and stochastic gradient descent. The experimental findings show that the proposed method, called FIDC, has achieved state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this paper, a motion video sequence extraction algorithm based on cumulative frame differential clustering is proposed for the same scene. Firstly, the difference results of the two frames are clustered, and then median filtering technology and morphological method are used to extract and process the clustered results for obtaining moving foreground objects. During the process of video object image extraction, the color space clustering algorithm and filtering algorithm are introduced to reduce color noise in image and improve the accuracy of target object location and extraction efficiency. Finally, the framework of extraction system are designed and implemented. The high performance and accuracy of the proposed method are proved by quantitative analysis and extraction results comparison. The research in this paper has theoretical and practical value for promoting the development and application of key frame extraction technology and content-based video retrieval. The experimental results show that the algorithm has better performance, in terms of low computational complexity, real-time performance and better segmentation results. [ABSTRACT FROM AUTHOR]

The paper reports on the radio propagation characteristics of Massive MIMO. The realistic measurements are conducted in typical outdoor LOS and NLOS scenarios with the bandwidth of 100 MHz at the carrier frequency of 1.4725 GHz. In this paper the channel propagation in spectrum and space domains are investigated by employing the high-precision parameter estimation algorithm. Based on big data technology, we propose the multipath clustering algorithm and subinterval programming to bring deeper insight into the cluster evolution over the antenna array axis. The works focus on the correlation, and the result is in accordance with the theory of the cluster's visible region. Furthermore, a non-WSSUS (non-wide sense stationary uncorrelated scattering) channel analytical model is established. The whole research work aims to contribute the radio channel modeling of the 5G Massive MIMO communication system. [ABSTRACT FROM AUTHOR]

With the transformation from traditional manufacturing to intelligent manufacturing, customer-oriented personalized customization has gradually become the main mode of production. Interactive algorithms determine the pros and cons of the solution via customers which can make customers better participants in the customization process. However, if the population size is expanded and the number of evolutionary iterations is too high, frequent interactions are likely to cause customer fatigue. This paper proposes an adaptive interactive artificial immune algorithm based on improved hierarchical clustering. This algorithm uses the improved hierarchical clustering algorithm to optimize generation of the initial antibodies and applies the affinity calculation method based on customer intention, adaptive crossover and mutation operators, and a multisolution reservation method based on hybrid selection strategy to the artificial immune algorithm. Via empirical research on the customized operational data of wheel hubs, the proposed method effectively solves the problem of customer fatigue, significantly improves the convergence speed of the algorithm and reduces the time cost. [ABSTRACT FROM AUTHOR]

An implicit limit state function is predicted with a support vector machine (SVM) for reliability analysis of engineering structures to reduce the number of finite element analyses. The accuracy and predictability of the SVM method are reduced considerably by noise in data. In this paper, density-based spatial clustering of applications with noise (DBSCAN) is applied to reduce the noise in training samples for SVM regression. Then, the SVM model is linked with Monte Carlo simulation (MCS) to find out the reliability of the engineering structures. Four different examples of static and dynamic problems are solved to show acceptability and efficiency of the proposed method. It is observed that the proposed method is suitable for a smaller number of performance function calls. Direct MCS, artificial neural network-based MCS and response surface methods have been used to examine the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity. In this research, multiple random Secondary Users (SUs), and PUs are considered for implementation. Hence, the proposed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algorithms. Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646% compared to the existing algorithms. Similarly, ASDIC algorithm reduced 24.23% of SUs average node power compared to the existing algorithms. Probability of detection is higher by reducing the Signal-to-Noise Ratio (SNR) to 2 dB values. The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection. Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of network capacity. [ABSTRACT FROM AUTHOR]

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Node mobility improves the self-deployment capability of the network; meanwhile, it also leads to frequent interruption of communication links and severe packet loss. Mitigating the negative impact of node movement on cluster stability is a new challenge. Existing clustering protocols try to use multi-hop data transmission, but they do not deal with the increase in communication overhead. This paper proposes a distributed node status judgment-based weighted clustering algorithm to solve the problems of easily broken communication links and excessive node reaffiliation in mobile wireless sensor networks (MWSNs). The protocol establishes intra-cluster second-level communication in order to solve the problem of the sudden interruption of dynamic communication links. A node status judgment algorithm was constructed to analyze the motion behavior of sensor nodes, distinguish the node states, and screen multiple communication nodes, thereby alleviating the transmission delay caused by complex routing. The extended Kalman filter (EKF) was used to filter the sensor noise in a non-ideal environment and to predict the actual position of nodes. The simulation results explain that the proposed protocol can effectively reduce node reaffiliation and the dominant set's update frequency when the node runs at medium and high speeds while simultaneously maintaining low energy consumption. [ABSTRACT FROM AUTHOR]

In online learning, students' learning data such as time and logs are commonly used to predict the student's learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a "prediction and alert" model for students' learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students' continuous online and offline learning more effectively. [ABSTRACT FROM AUTHOR]

Force-directed algorithm is one of the most commonly used methods for visualization of 2D graphs. These algorithms can be applied to a plethora of applications such as data visualization, social network analysis, crypto-currency transactions, and wireless sensor networks. Due to their effectiveness in visualization of topological data, various force-directed algorithms for 2D graphs were proposed in recent years. Although force-directed algorithms for 2D graphs were extensively investigated in research community, the algorithms for 3D graph visualization were rarely reported in the literature. In this paper, we propose four novel clustering-based force-directed (CFD) algorithms for visualization of 3D graphs. By using clustering algorithms, we divide a large graph into many smaller graphs so that they can be effectively processed by force-directed algorithms. In addition, weights are also introduced to further enhance the calculation for clusters. The proposed CFD algorithms are tested on 3 datasets with varying numbers of nodes. The experimental results show that proposed algorithms can significantly reduce edge crossings in visualization of large 3D graphs. The results also reveal that CFD algorithms can also reduce Kamada and Kawai energy and standardized variance of edge lengths in 3D graph visualization. [ABSTRACT FROM AUTHOR]

The border security situation is complex and severe, and the border patrol system relying on the ground-air cooperative architecture has been paid attention to by all countries as an important means of protecting national security. In the flying ad-hoc network (FANET), under the ground-air cooperative architecture, an unmanned aerial vehicle (UAV) uses a patrol mobility model to improve patrol efficiency. Since the patrol mobility model leads to frequent changes in UAV movement direction to improve patrol efficiency, selecting some clustering utility factors and calculating utility factors in previous clustering algorithms do not apply to this scenario. To solve the above problems, in this paper, we propose a border patrol clustering algorithm (BPCA) based on the ground-air cooperative architecture, which is based on the existing weighted clustering algorithm and improved in terms of the selection of utility factors and calculations of utility factors in cluster head selection. This algorithm comprehensively considers the effects of relative speed, relative distance, and the movement model of the UAV on the network topology. Extensive simulation results show that this algorithm can extend the duration time of cluster heads and cluster members and improve the stability of clusters and the reliability of links. [ABSTRACT FROM AUTHOR]

An attribute category clustering method based on hierarchical clustering is proposed in order to study the big data intelligent analysis and processing technology. The proposed model combines the attribute categories with similar fault type distribution, reduces the data dimension, and binarizes it. To address the problem of more missing values of continuous data, a data completion method based on attribute distribution function is adopted. Through the perspective of selection and estimation of project unit price in construction enterprises, this paper summarizes the data mining process facing the characteristics of project cost data, and puts forward the method of analyzing and processing project cost data based on clustering algorithm. Finally, the processed data sets are subjected to bottom-up hierarchical clustering analysis, and finally the ideal analysis results can be obtained. The experimental results show that the preprocessing method based on attribute clustering proposed in this paper can effectively merge attributes, reduce the dimension after binary transformation and effectively reduce the amount of data under the condition of ensuring data information. [ABSTRACT FROM AUTHOR]