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Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry. [ABSTRACT FROM AUTHOR]

In order to solve the cold-starting problem and improve the intake and exhaust pipe temperatures of diesel engines under cold-starting and low- and medium-speed conditions, this paper proposes a twice-open control method for a hydraulic variable valve system. First, a hydraulic variable valve system that can realize a fully variable valve lift and phase angle is applied to replace the original intake system in order to meet the air intake requirements of different conditions. Then, a twice-open control method in which the intake valve opens two times at the exhaust stroke and intake stroke is proposed to improve the intake pipe temperature and solve the cold-starting problem. This paper contains a numerical work analysis. A GT-POWER model is constructed to validate the intake valve twice-open control method. The cylinder pressure, cylinder temperature, intake pipe pressure, and intake pipe temperature are obtained and compared between the original intake valve system and the hydraulic variable valve system with the proposed intake valve twice-open control method. The results show that the twice-open control method can increase the intake pipe temperature to 260 K or even higher, which can improve the cold-starting performance and the exhaust temperature at low and medium speeds. At the same time, the performance under low- and medium-speed conditions is improved. [ABSTRACT FROM AUTHOR]

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods. [ABSTRACT FROM AUTHOR]

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms. [ABSTRACT FROM AUTHOR]

The classical linear discriminant analysis (LDA) algorithm has three primary drawbacks, i.e., small sample size problem, sensitivity to noise and outliers, and inability to deal with multi-modal-class data. This paper reviews LDA technology and its variants, covering the taxonomy and characteristics of these technologies and comparing their innovations and developments in addressing these three shortcomings. Additionally, we describe the application areas and emphasize the kernel extensions of these technologies to solve nonlinear problems. Most importantly, this paper presents perspectives on future research directions and potential research areas in this field. [ABSTRACT FROM AUTHOR]

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields. [ABSTRACT FROM AUTHOR]

The conventional PI control, which has been extensively employed in the high-performance control of linear induction motors, has the benefit of a straightforward concept, quick dynamic response, and simple control. Nevertheless, the linear induction motor is more susceptible to outside disturbances and mismatched parameters since it is a highly linked, high-order, nonlinear, time-varying, complex system. Because of this, this paper proposes a double-closed-loop control of the linear induction motor, whose speed and current loops are the model-predictive speed controller and the model-predictive current controller, respectively, to solve the problems of speed overshoot and oscillation when the linear induction motor is operated under PI control. The findings demonstrate the benefits of the suggested control strategy, which significantly enhances the linear induction motor's speed control performance. These benefits include improved dynamic performance, limited overshooting, and robust resistance to load disturbance. [ABSTRACT FROM AUTHOR]

Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement. [ABSTRACT FROM AUTHOR]

In the realm of existing intelligent drip irrigation control systems, traditional PID control encounters challenges in delivering satisfactory control outcomes, primarily owing to issues related to non-linearity, time-varying behavior, and hysteresis. In order to solve the problem of the unstable operation of the drip irrigation system in an intelligent irrigation system, this paper proposes chaotic beetle swarm optimization (CBSO) based on the BAS (beetle antennae search) longicorn search algorithm, with inertial weights, variable learning factors, and logistic chaos initialization improving global search capabilities. This was accomplished by formulating the optimization objective, which involved integrating the control input's time integral term, the square term, and the absolute value of the error. Subsequently, PID parameter tuning was performed. In order to verify the actual effect of the CBSO algorithm on the PID drip irrigation control system, MATLAB was used to simulate and compare PID control optimized by the GA algorithm, PSO algorithm, and BSO (beetle search optimization) algorithm. The results show that PID control based on CBSO optimization has a short response time, small overshoot, and no oscillation in the steady state process. The performance of the controller is improved, which provides a basis for PID parameter setting for a drip irrigation control system. [ABSTRACT FROM AUTHOR]

Reducing carbon emission and raising efficient production are the important goals of modern enterprise production process. The same product can be produced by a variety of equipment, and the carbon emissions and processing time of different equipment vary greatly. Choosing suitable production equipment is an important method for manufacturing enterprises to achieve the efficient emission reduction of production process. However, the traditional production equipment selection mode only gives qualitative results, and it is difficult to provide effective advice for enterprises to choose suitable equipment under the needs of carbon neutrality. To solve this problem, this paper systematically analyzes carbon emission and the time of the turning production process, and a unified calculation model for carbon emission and efficient production of multi-type processing equipment is established. The important point of the article is to research the diversity among between carbon emissions and efficiency levels of the same product produced by different devices. The carbon emissions and efficiency levels of different kinds of equipment can be calculated by the BAS algorithm. By turning a shaft part as an example, the results show that this method can calculate the optimal value of carbon emissions and efficiency of the same product produced by different equipment and can provide suggestions for enterprises to select appropriate equipment for low-carbon and efficient production. This paper provides a reference for further research on the quantitative calculation model for the selection of high-efficiency and low-carbon production equipment. [ABSTRACT FROM AUTHOR]

In the flexible manufacturing system deadlock prevention domain, researchers' almost final target is to seek the maximally permissive controllers for solving the deadlock problems of flexible manufacturing systems. However, it seems a challenging work. Whatever you adopt, what kinds of methods, policies, and strategies, it seems complicated to obtain optimal controllers for deadlock prevention even if they claim their algorithms are optimal until the deadlock recovery is developed. Therefore, many experts, including us, have decided to design all kinds of actual maximally permissive recovery policies based on control transitions. It is a pity that these policies usually solve some particular flexible manufacturing systems' deadlock problems. The controllers failed to recover the deadlock situation of flexible manufacturing systems once the idle or resource places were changed. In other words, these policies could never know or identify the real number of maximally permissive controllers in the same structure with different idle places. The precise combination of the number of idle places is called the saturated line, and it divides the reachability states into two region (saturated region and unsaturated region). Accordingly, this paper proposes a novel concept that achieves the precise maximum permissiveness and fits all kinds of flexible manufacturing systems. The experimental results show how our policy does in practice. [ABSTRACT FROM AUTHOR]

The four-way-shuttle-based storage and retrieval system is a recent innovative intelligent vertical warehousing system that has been widely applied in manufacturing and e-commerce environments due to its high flexibility and density. As a complex multi-device cooperative operational system, this system features the parallel operation of multiple elevators and four-way shuttles. During large-scale-batch inbound operations, the quality of scheduling solutions for inbound-operation equipment significantly impacts the system's efficiency and performance. In this paper, a detailed analysis of the inbound-operation process in the system is conducted, taking into consideration the motion characteristics of both the elevators and four-way shuttles. Furthermore, we establish operational time constraints that account for equipment acceleration and deceleration characteristics and introduce a flexible flow-shop-scheduling model to address the scheduling problem in the system. Additionally, we propose an improved genetic algorithm based on double-layer encoding to solve this problem. Comparative experiments with a traditional genetic algorithm and ant-colony algorithm demonstrate the superior efficiency and accuracy of our approach. Finally, the effectiveness of the proposed algorithm is validated through comparisons with large-scale practical experiments. [ABSTRACT FROM AUTHOR]

With the increasing installed capacity of renewable energy in the energy system, the uncertainty of renewable energy has an increasingly prominent impact on power system planning and operation. Renewable energy such as wind and solar energy is greatly affected by the external weather. How to use a reasonable method to describe the relationship between weather and renewable energy output, so as to measure the uncertainty of renewable energy more accurately, is an important problem. To solve this problem, this paper proposes a renewable energy scenario generation method based on a conditional generation countermeasure network and combination weighting method (CWM-CGAN). In this method, the combination of AHP and the entropy weight method is used to analyze the meteorological factors, the weather classification is defined as the condition label in the conditional generation countermeasure network, and the energy scenario is generated by the conditional generation confrontation network. In this paper, the proposed method is tested with actual PV data, and the results show that the proposed model can describe the uncertainty of PV more accurately. [ABSTRACT FROM AUTHOR]

Recently, a new swarm intelligence optimization algorithm called the remora optimization algorithm (ROA) was proposed. ROA simulates the remora's behavior of the adsorption host and uses some formulas of the sailfish optimization (SFO) algorithm and whale optimization algorithm (WOA) to update the solutions. However, the performance of ROA is still unsatisfactory. When solving complex problems, ROA's convergence ability requires further improvement. Moreover, it is easy to fall into local optimization. Since the remora depends on the host to obtain food and optimize ROA performance, this paper introduces the mutualistic strategy to strengthen the symbiotic relationship between the remora and the host. Meanwhile, chaotic tent mapping and roulette wheel selection are added to further improve the algorithm's performance. By incorporating the above improvements, this paper proposes an improved remora optimization algorithm with a mutualistic strategy (IROA) and uses 23 benchmark functions in different dimensions and CEC2020 functions to validate the performance of the proposed IROA. Experimental studies on six classical engineering problems demonstrate that the proposed IROA has excellent advantages in solving practical optimization problems. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) has been extensively adopted for multi-objective optimization due to its efficient and straightforward framework. In DE, the mutation operator influences the evolution of the population. In this paper, an adaptive Grid-based Multi-Objective Differential Evolution is proposed to address multi-objective optimization (ad-GrMODE). In ad-GrMODE, an adaptive grid environment is employed to perform a mutation strategy in conjunction with performance indicators. The grid reflects the convergence and diversity performance together but is associated with the user-specified parameter "div". To solve this problem, we adaptively tune the parameter "div". Among the DE mutation strategies, "DE/current-to-best/1" is applied extensively in single-objective optimization. This paper extends the application of "DE/current-to-best/1" to multi-objective optimization. In addition, a two-stage environmental selection is adopted in ad-GrMODE, where in the first stage, one-to-one selection between the parent and its corresponding offspring solution is performed. In addition, to preserve elitism, a stochastic selection is adopted with respect to performance metrics. We conducted experiments on 16 benchmark problems, including the DTLZ and WFG, to validate the performance of the proposed ad-GrMODE algorithm. Besides the benchmark problem, we evaluated the performance of the proposed method on real-world problems. Results of the experiments show that the proposed algorithm outperforms the eight state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Considering the uniformity of cable tension variation, in this paper, the trajectory planning problem of the exit point for a continuously reconfigurable four-cable-driven two-degrees-of-freedom (DOF) parallel mechanism was studied. Furthermore, an improved quadratic programming model-based trajectory planning method is proposed, which greatly reduces the change in cable tension and can be used to solve the problem of excessive cable tension change when the existing mechanism moves on the moving platform. First, the structural characteristics of the parallel mechanism with a fixed exit point were analyzed, and the static model was established. Considering the cable length and tension constraints, the feasible workspace of the mechanism force was solved. Then, based on the dynamic modeling, an improved quadratic programming model was used to solve the cable tension values under the typical trajectory in the force-feasible workspace. Finally, considering the influence of structural parameters on the change in cable tension, the improved quadratic programming model was transformed, and an exit point trajectory planning model was proposed. The uniform change in cable tension was realized by continuously changing the exit point position. The results show that the cable tension can change uniformly in a very small range by planning the trajectory of the exit point, and the stability of the moving platform movement is guaranteed to the greatest extent. [ABSTRACT FROM AUTHOR]

At present, there are a large number of growing medical applications in the application market. It is difficult for users to find satisfactory medical services conveniently and efficiently. The classical collaborative filtering algorithm has some problems, such as cold start, unsatisfactory recommendation results, and so on. This paper proposes a hybrid medical service recommendation approach based on knowledge graph to solve the above problems. This approach introduces the open knowledge graph and establishes the semantic link relationship between the mobile application and the knowledge graph entity. It not only enhances the semantic feature of single application for improving the accuracy of recommendation results, but also realizes the in-depth analysis of the semantic relationship among multiple application entities in the knowledge graph through the TransHR model which can alleviate the cold start problem. Then we design a hybrid recommendation algorithm based on multi-dimensional similarity fusion. This algorithm uses the entropy method to organically integrate the calculation results of multi-dimensional semantic similarity, such as feature vector similarity, entity relation similarity, and user rating similarity. It is convenient and efficient to recommend satisfactory medical application services to target users. Finally, we test and analyze the accuracy and effectiveness of our proposed approach by experiment. [ABSTRACT FROM AUTHOR]

In the waterflooding development of fractured ultra-low permeability reservoirs, the heterogeneity is becoming increasingly serious. The development of large fracture channels leads to serious water channelling and low recovery, and the effect of conventional profile control is not ideal. This paper proposed gel foam composite profile control and flooding technology to solve the above problems. Herein, the new intelligent gel and foaming agent systems were optimized through laboratory experiments, and their performance was evaluated. The new intelligent gel system has the characteristics of low viscosity, easy preparation, good injection, slow cross-linking, high strength, and long-term effectiveness. The injection parameters were optimized, and the indoor injection scheme was formulated, that is, the optimal injection volumes of gel and foam slugs were 0.3 and 0.6 PV, respectively. The injection sequence of composite slugs was to inject gel slugs first, then foam slugs. The injection mode of air foam slugs was multiple rounds of small slug injection. The final recovery rate in the indoor dual tube oil displacement experiment reached 35.01%, increasing by 23.69%. Furthermore, an oil output increase of 899 t and an average water cut decrease of 5% were acquired in the oil field test. It shows that the injection scheme can effectively improve oil recovery. The gel foam compound profile control and flooding technology herein has good adaptability in similar reservoirs and has good promotion prospects. [ABSTRACT FROM AUTHOR]

The power quality estimation for distribution network connected DG (distributed generation) is important in the power system. The significance testing for power quality indicator is less used in traditional power quality evaluation. However, the power quality indicator is affected by various factors of the power system, which seriously impact the power quality evaluation result. To solve this problem, A novel power quality comprehensive estimation model based on multi-factor variance analysis for distribution network with DG is proposed in this paper, in which the significance testing is carried out for power quality indicator with the various system factors, and then to generate the evaluation weights in different levels, further to obtain the power quality assessment results for single node. And then, the dual-significance tests are carried out to generate the weight of node and to obtain the comprehensive estimation result of whole system. At last, an example is developed to validate that, compared with the traditional power quality evaluation, the proposed method is more reasonable and effective in the power quality evaluation for DG connected distribution network. [ABSTRACT FROM AUTHOR]

The surface defects of industrial structural parts have the characteristics of a large-scale span and many small objects, so a novel YOLOv3 model, the YOLOv3-ALL algorithm, is proposed in this paper to solve the problem of precise defect detection. The K-means++ algorithm is combined with the intersection-over-union (IoU) and comparison of the prior box for clustering, which improves the clustering effect. The convolutional block attention module (CBAM) is embedded in the network, thus improving the ability of the network to obtain key information in the image. By adding fourth-scale prediction, the detection capability of a YOLOv3 network for small-object defects is greatly improved. A loss function is designed, which adds the generalized intersection-over-union (GIoU) loss combined with focal loss to solve the problems of L2 loss and class imbalance in samples. Experiments regarding contour-defect detection for stamping parts show that the mean average precision (mAP) of the YOLOV3-ALL algorithm reaches 75.05% in defect detection, which is 25.16% higher than that of the YOLOv3 algorithm. The average detection time is 39 ms/sheet. This proves that the YOLOv3-ALL algorithm has good real-time detection efficiency and high detection accuracy. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making (MADM) refers to making preference decisions via assessing a finite number of pre-specified alternatives under multiple and usually conflicting attributes. Many problems in the field of additive manufacturing (AM) are essentially MADM problems or can be converted into MADM problems. Recently, a variety of MADM methods have been applied to solve MADM problems in AM. This generates a series of interesting questions: What is the general trend of this research topic from the perspective of published articles every year? Which journals published the most articles on the research topic? Which articles on the research topic are the most cited? What MADM methods have been applied to the field of AM? What are the main strengths and weaknesses of each MADM method used? Which MADM method is the most used one in this field? What specific problems in AM have been tackled via using MADM methods? What are the main issues in existing MADM methods for AM that need to be addressed in future studies? To approach these questions, a review of MADM methods in AM is presented in this paper. Firstly, an overview of existing MADM methods in AM was carried out based on the perspective of specific MADM methods. A statistical analysis of these methods is then made from the aspects of published journal articles, applied specific methods, and solved AM problems. After that, the main issues in the application of MADM methods to AM are discussed. Finally, the research findings of this review are summarised. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective collaborative optimization (MOCO) strategy is proposed for making decisions on a distillation column group. Firstly, based on data preprocessing, the operating modes of the tower group are determined by use of the fuzzy C-means clustering method. Secondly, based on the proposed concept of a collaborative variable, the discrete state-space model of the main towers are constructed by the subspace identification method. Then, a MOCO optimization model is designed for the ethylene plant. Finally, NSGA-III is used to solve the optimization problem. An analysis of a Pareto-optimal frontier and population is carried out. To illustrate the superiority of the proposed strategy, the results are compared with historical data and the appealing operation area is finally obtained. [ABSTRACT FROM AUTHOR]

Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm. [ABSTRACT FROM AUTHOR]

The high-performance control technology of multi-phase motors is a key technology for the application of multi-phase motors in many fields, such as electric transportation. The model predictive current control (MPCC) strategy has been extended to multi-phase systems due to its high dynamic performance. Model-predictive current control faces the problem that it cannot effectively regulate harmonic plane currents, and thus cannot obtain high-quality current waveforms because only one switching state is applied in a sampling period. To solve this problem, this paper uses the virtual vector-based MPCC to select the optimal virtual vector and apply it under the premise that the average value of the harmonic plane voltage in a single switching cycle is zero. Taking a five-phase induction motor as an example, the steady-state and dynamic performance of the proposed virtual vector MPCC and the traditional model predictive current control were simulated, respectively. Simulation results demonstrated the effectiveness of the proposed method in improving waveform quality while maintaining excellent dynamic performance. [ABSTRACT FROM AUTHOR]

The Lamadian oilfield has entered the stage of strong water cut after natural energy development and conventional water flooding development. The use of asphalt binder for profile control can not only adjust the contradiction between layers, expand the swept volume, but also improve the oil displacement efficiency. The field test has achieved certain results. The main oil layer in the Lamadian oilfield has a strong oil layer thickness and serious vertical and plane heterogeneity. After years of water injection development and polymer injection development, most oilfields have entered a period of strong water cut. In the test, it is found that the effect of different well layers is very different, the effect is unstable, and the reason is unclear. Therefore, it is necessary to carry out research on the adaptability and parameter optimization of profile control of asphalt binder through laboratory experiments. In this paper, the asphalt binder provided on site are modified and the dispersion effect of modified asphalt binder is studied, and the concentration of suspending agent is optimized. The artificial cemented core structure and injection method are improved to solve the problem of aggregated asphalt binder on the end face during injection. The displacement profile control experiment was carried out with artificial cores, and the matching relationship between the injected particle size of the asphalt binder and the permeability was determined, and the optimal injection amount was optimized for cores with different permeabilities. The research results show that adding a KCl (potassium chloride solution) solution with a concentration of 2% as a dispersant can exert a better dispersing effect on the asphalt binder. Through the plugging rate experiments of three types of asphalt binder, the profile control effect is determined as the best when the particle size of the asphalt binder is 0.06–0.1 mm. According to the experimental results, the experimental research on the injection concentration and profile control radius of the profile control agent system was carried out. Finally, it was determined that the injection concentration of 3500 mg/L and the profile control radius of 1/3–1/2 of the well spacing were the optimal injection parameters. The field application of a profile control agent provides experimental basis. [ABSTRACT FROM AUTHOR]

Maritime autonomous surface ships (MASS) are proposed as a future technology of the maritime industry. One of the key technologies for the development of MASS is condition-based maintenance (CBM) based on prognostics and health management (PHM). The CBM technology can be used for early detection of abnormalities based on the database and for a prediction of the fault occurring in the future. However, this technology has a problem that requires a high-quality database that reproduces the operation state of the actual ships and quantitatively and systematically indicates the characteristics for the various fault state of the device. To solve this problem, this paper presents a study on the development method of the fault database based on the reliability. Firstly, the reliability analysis of the target device was performed to select five types of the core fault modes. After that, a fault simulation scenario that defined the fault simulation test methodology was drawn. A land-based testbed was built for the fault simulation test. The fault simulation database was developed with a total of 109 sets through the fault simulation test. Additionally, a fault classification algorithm based on deep learning is proposed. The classification performance was evaluated with a confusion matrix. The developed database will be expected to serve as the basis for the development CBM technology of MASS in the future. [ABSTRACT FROM AUTHOR]

Continual efforts to have better processes lead us to search for new ways to improve and innovate. One of the most powerful approaches to innovating technical systems is the TRIZ (Theory of Inventive Problem Solving). TRIZ is, unfortunately, very hard to learn and adequately use. This paper introduces a new comprehensive algorithm for the innovation of processes in production based on TRIZ principles. The Algorithm for Process Innovation by Increasing Ideality (AP3I) helps search for innovative ways to improve or change the process—either the whole process or its segments. Besides the original TRIZ, AP3I is easier to use and might be applied by engineers in industrial practice. On the other hand, results from AP3I are probably weaker compared to full TRIZ. Still, the AP3I can be very helpful in efforts to improve processes and can provide powerful ideas. The overall algorithm is also demonstrated in case studies on processes of packing and assembly. [ABSTRACT FROM AUTHOR]

This paper associates with stability analysis of linear impulsive stochastic delay systems (ISDSs). Although many conclusions about the stability of ISDSs have been obtained based on Lyapunov's method, relatively few research theories about delay-dependent stability with less conservativeness have been established. Therefore, we introduce an appropriate Lyapunov-Krasovskii functional (LKF) to work out this problem, and a novel delay-dependent exponential stability theorem is first deduced. On the other hand, when mean-square stability is considered, we present delay-dependent stability conditions, it is of interest to note that the proposed conditions do not depend on the size of delays in the diffusion term, which solves the problems of determining the mean-square stability of ISDSs for which the diffusion term delays are not available. In the end, two numerical examples are carried out to verify the feasibility of our conclusions. [ABSTRACT FROM AUTHOR]

Assessment of process stability is a key to ensuring the high quality of any product, service, activity, etc. The main tool for doing this is a Shewhart control chart. However, it becomes difficult to analyze hundreds or thousands of control charts at a time for some up-to-date complex processes. In order to solve this problem, various indicators of process stability have been recently proposed with the Stability Index (SI) being the latest one, and, in many opinions, the best one. We have thoroughly analyzed different suggestions and found many of them ambiguous and sometimes even harmful to practitioners. Some examples of possible problems with the SI are provided below. It is argued that abnormal values of the SI can be caused not only by process instability, but by non-homogeneity, non-randomness, nonnormality, and autocorrelation as well. It is proved that the SI can be helpful for practitioners as an index to attract primary attention to the process. A proposal to rename the SI and widen significantly its application is given. Finally, some ways that practitioners could use this index more effectively are discussed. [ABSTRACT FROM AUTHOR]

An interval type-2 (IT-2) fuzzy control design method is developed to solve the formation and containment problem of nonlinear multi-boiler systems. In most practical industrial systems such as airplanes, vessels, and power plants, the boiler system often exists as more than one piece of equipment. An efficient control theory based on the leader-following multi-agent system is applied to achieve the control purpose of multiple boiler systems simultaneously. Moreover, a faithful mathematical model of the nonlinear boiler system is extended to construct the multi-boiler system so that the dynamic behaviors can be accurately presented. For the control of practical multi-agent systems, the uncertainties problem, which will deteriorate the performance of the whole system greatly, must be considered. Because of this, the IT-2 Takagi–Sugeno (T–S) fuzzy model is developed to represent the nonlinear multi-boiler system with uncertainties more completely. Based on the fuzzy model, the IT-2 fuzzy formation and containment controllers are designed with the imperfect premise matching scheme. Thus, the IT-2 fuzzy control method design can be more flexible for the nonlinear multi-boiler system. Solving the formation problem, a control method without the communication between leaders differs from the previous research. Since leaders achieve the formation objective, the followers can be forced into the specific range formed by leaders. Via the IT-2 fuzzy control method in this paper, not only can the more flexible process of the controller design method be developed to solve the uncertainties problem magnificently, but a more cost-effective control purpose can also be achieved via applying the lower rules of fuzzy controllers. Finally, the simulation results of controlling a nonlinear multi-boiler system with four agents are presented to demonstrate the effectiveness of the proposed IT-2 fuzzy formation and containment control method. [ABSTRACT FROM AUTHOR]

The S reservoir in the X Oilfield in Iraq has great development potential due to its rich geological reserves. However, the low permeability and strong heterogeneity of the reservoir lead to great differences in reservoir stimulation performance. In this study, an integrated reservoir model and differential stimulation mode are put forward to solve the above problems. First, the feasibility of fracturing is evaluated by laboratory experiments. Second, an integrated reservoir model is established, which mainly includes a rock mechanics model, fracturing simulation model, and numerical simulation model, and correct the integrated model by fracturing operation curves and production dynamic curves. Third, three types of stimulation areas are classified according to the combination of sweet spot types, and three different stimulation modes are proposed. In conclusion, a small-scale stimulation mode should be applied in the Type I area to maximize economic benefits. In the Type II area, the medium-scale stimulation mode should be performed to ensure certain productivity while achieving certain economic benefits. In the Type III area, the large-scale stimulation mode should be employed to obtain certain productivity while economic benefits must be above a limit. The differential stimulation model proposed in this paper has made a great reference for the efficient development of low-permeability carbonate rocks. [ABSTRACT FROM AUTHOR]

This paper considers the constrained inverse problem based on the nonlinear convection-diffusion equation in the multiphase porous media flow. To solve this problem, a widely convergent homotopy method is introduced and proposed. To evaluate the performance of the mentioned method, two numerical examples are presented. This method turns out to have wide convergence region and strong anti-noise ability. [ABSTRACT FROM AUTHOR]

Based on both theoretical and practical experiences, the measures aimed at controlling emergency shutdowns of stopes have been highlighted. These stopes are connected with the emergency rigid settlements of powered complexes. In terms of the Western Donbas mines, there are certain risks of a shutdown of stopping operations within the zone of primary roof caving. Thus, the causes of emergency rigid settlements of the support may include the following: layers of the main roof rocks are hanging and not timely delaminated; sudden changes in lithology; hydraulic overloading of the main roof; structural flaws of support under certain conditions of its use, etc. In this paper, the theoretical method of scientific cognition was applied, which, with its help, makes it possible to switch from single low-efficiency measures to a set of actions aimed at preventing any uncontrolled situation. Obtaining certain generalized knowledge means obtaining a much deeper representation of reality, penetrating into its essence. The study also involves statistical analysis, being the basis for outlining a zone of primary caving where a high degree of risk is observed. Certainly, the generalization of these measures does not solve the problem completely. Consequently, there will be further attempts to search for and achieve principal new solutions in the future. [ABSTRACT FROM AUTHOR]

To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%. [ABSTRACT FROM AUTHOR]

The k-nearest neighbor (kNN) method only uses samples' paired distance to perform fault detection. It can overcome the nonlinearity, multimodality, and non-Gaussianity of process data. However, the nearest neighbors found by kNN on a data set containing a lot of outliers or noises may not be actual or trustworthy neighbors but a kind of pseudo neighbor, which will degrade process monitoring performance. This paper presents a new fault detection scheme using the mutual k-nearest neighbor (MkNN) method to solve this problem. The primary characteristic of our approach is that the calculation of the distance statistics for process monitoring uses MkNN rule instead of kNN. The advantage of the proposed approach is that the influence of outliers in the training data is eliminated, and the fault samples without MkNNs can be directly detected, which improves the performance of fault detection. In addition, the mutual protection phenomenon of outliers is explored. The numerical examples and Tenessee Eastman process illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The dynamic simulation of the continuous catalytic reforming process is of great significance to the performance prediction and optimization of the entire process. In this study, a 34-lumped mechanism model described by differential algebra was established based on the actual process conditions of the continuous catalytic reforming process in China, and an efficient dynamic simulation solution method based on simultaneous equations was proposed. First, a 34-lumped differential–algebraic mechanism model was established based on the basic principles of reforming kinetics, thermodynamics, material balance, and energy balance. Secondly, in order to solve and simulate the mechanism model composed of 144 differential equations and several algebraic equations, the method of finite-element collocation is used to discretize the differential equations and convert them into large-scale, nonlinear programming problems, and the interior point algorithm is used to estimate its parameters and verify the model. In addition, in order to avoid the problem of too long derivative solution time and too large memory in the solution process, methods such as sparse derivative and Broyden–Fletcher–Goldfarb–Shanno (BFGS) with limited storage are used to solve the problem. Finally, on the basis of model verification, dynamic simulation and sensitivity analysis of the whole process are carried out by modifying different input parameters. The results show that the mechanism model and solution method presented in this paper can quickly and accurately simulate the continuous catalytic reforming process dynamically. [ABSTRACT FROM AUTHOR]

Based on the panel data of 224 prefecture-level and above cities in China from 2003 to 2016, this paper empirically studies the impact of urban sprawl on air pollution and introduces a mediating effect model to test the mediating role of vehicle ownership concerning the impact of urban sprawl on air pollution. The research in this paper arrives at three conclusions. First, urban sprawl has a significant positive effect on air pollution, and this conclusion is still valid after solving the endogeneity problem and conducting a robustness test. Second, the results of mediating effect test show that urban sprawl indirectly affects air pollution through the partial mediating effect of vehicle ownership. By removing the mediating effect, urban sprawl has a significant negative impact on air pollution, indicating that the mediating effect of vehicle ownership is higher concerning the impact of urban sprawl on air pollution. Third, further panel quantile regression results show that the higher the level of air pollution, the weaker the mediating effect of vehicle ownership and the stronger the direct effect of urban sprawl on air pollution. These conclusions can provide some empirical support for solving the air pollution problems caused by urban sprawl in China. [ABSTRACT FROM AUTHOR]

This paper studies the production process of a shipbuilding enterprise. The company suffers from long manufacturing cycle, low utilization rate of personnel and an unbalanced production line. To solve these problems, the lean shipbuilding mode, mainly divided into shipbuilding work breakdown, production plan and virtual flow operation in this paper, is put forward, which combines the lean production and modern information management technology with shipbuilding. Supported by the theory of work breakdown structure and task package scheduling, the shipbuilding task package is reasonably divided. The priority of task package manufacturing is determined by calculating the task package manufacturing sequence coefficient, and a reasonable number of operators is calculated to ensure the continuity of segmented manufacturing. After determining the manufacturing priority of the task pack and the number of allocable personnel, the corresponding work can be scheduled. Production planning drives all production activities of the shipbuilding enterprise, and just-in-time production is achieved through the reasonable arrangement of these production plans, thus reducing the waste of personnel and time. Then, the virtual flow operation is carried out, which can achieve high efficiency of flow production and high flexibility of fixed workstation production during the production process of large-scale and heavy-duty products. The virtual assembly production system of the workshop is established according to the characteristics of shipbuilding operation and the actual production situation. On this basis, a lean shipbuilding manufacturing execution system for small and medium-sized shipbuilding enterprises is developed to achieve lean production in a shipbuilding workshop. Through the implementation of the lean shipbuilding mode based on task package scheduling and its manufacturing execution system, compared with the original data, the ship production cycle is reduced to 76.7%, the number of workers is reduced by 16.7% and the production balance rate is up to 81%. [ABSTRACT FROM AUTHOR]

As China proposes to achieve carbon peak by 2030 and carbon neutrality by 2060, as well as the huge pressure on the power grid caused by the load demand of the energy supply stations of electric vehicles (EVs), there is an urgent need to carry out comprehensive energy management and coordinated control for EVs' energy supply stations. Therefore, this paper proposed a two-step intelligent control method known as ISOM-SAIA to solve the problem of the 24 h control and regulation of green/flexible EV energy supply stations, including four subsystems such as a photovoltaic subsystem, an energy storage subsystem, an EV charging subsystem and an EV battery changing subsystem. The proposed control method has two main innovations and contributions. One is that it reduces the computational burden by dividing the multi-dimensional mixed-integer programming problem of simultaneously optimizing the 24 h operation modes and outputs of four subsystems into two sequential tasks: the classification of data-driven operation modes and the rolling optimization of operational outputs. The other is that proper carbon transaction costs and carbon emission constraints are considered to help save costs and reduce carbon emissions. The simulation analysis conducted in this paper indicates that the proposed two-step intelligent control method can help green/flexible EV energy supply stations to optimally allocate energy flows between four subsystems, effectively respond to peak shaving and valley filling of power grid, save energy costs and reduce carbon emissions. [ABSTRACT FROM AUTHOR]

We analytically investigated the magnetohydrodynamic motions of electrically conductive, incompressible Oldroyd-B fluids through an infinite circular cylinder filled with a porous medium. A general expression was established for the dimensionless velocity of fluid as a cylinder moves along its symmetry axis with an arbitrary velocity; the expression can generate exact solutions for any motion of this fluid type, solving the discussed problem. Special cases were considered and validated through graphical investigation to illustrate important characteristics of fluid behavior. In application, this is the first presentation of an exact general expression for non-trivial shear stress related to the magnetohydrodynamic motions of Oldroyd-B fluids when a longitudinal time-dependent shear stress is applied to the fluid by a cylinder. Solutions for the motions of rate-type fluids are lacking. The graphical representations show that in the presence of a magnetic field or porous medium, fluids flow more slowly and the steady state is reached earlier. [ABSTRACT FROM AUTHOR]

With the booming development of the electricity market, market factors such as electricity trading varieties are growing rapidly. The frequency of transactions has become increasingly real-time, and transaction clearing and settlement tasks have become more complex. The increasing demands for concurrent access and carrying capacity in trading systems have made it increasingly difficult for existing systems to support business. This article proposes a transaction support system for large-scale electricity trading market entities, which solves the problems of high concurrency access and massive access data calculation while ensuring system security through business isolation measures. The system uses microservices to treat various functional modules as independent service modules, thus making service segmentation and composition more flexible. By using read–write separation, caching mechanisms, and several data reliability assurance measures, data can be stored and accessed quickly and securely. The use of a three-layer load balancing module consisting of an OpenResty access entry layer, a gateway routing gateway layer, and a WebClient service inter-resource invocation layer can effectively improve the system's ability to handle concurrent access. [ABSTRACT FROM AUTHOR]

Aiming at the problem of uneven clustering and the unreasonable energy consumption of LEACH protocol in the perception layer of IoT-based microgrids of static nodes; in this paper, we propose a stationary-node energy-based routing protocol (SERP). First, we select a dynamic cluster radius for clustering to meet the actual needs of the network during clustering. Then, to solve the problem that the number of cluster heads is difficult to determine, a dynamic optimal cluster head ratio is adopted. The dynamic optimal cluster head ratio can be obtained by minimizing the total energy consumption of cluster formation and the stable transmission phase, which can improve the efficiency of network transmission. Finally, by setting the residual energy factor and distance factor to improve the calculation of the cluster head election threshold, the energy load of the network is more uniform, and the location of the cluster head is more reasonable. Compared with the LEACH protocol and the HEED protocol, the simulation results show that the SERP protocol can effectively prolong the lifetime of the whole network. [ABSTRACT FROM AUTHOR]

As the object detection dataset scale is smaller than the image recognition dataset ImageNet scale, transfer learning has become a basic training method for deep learning object detection models, which pre-trains the backbone network of the object detection model on an ImageNet dataset to extract features for detection tasks. However, the classification task of detection focuses on the salient region features of an object, while the location task of detection focuses on the edge features, so there is a certain deviation between the features extracted by a pretrained backbone network and those needed by a localization task. To solve this problem, a decoupled self-attention (DSA) module is proposed for one-stage object-detection models in this paper. A DSA includes two decoupled self-attention branches, so it can extract appropriate features for different tasks. It is located between the Feature Pyramid Networks (FPN) and head networks of subtasks, and used to independently extract global features for different tasks based on FPN-fused features. Although the DSA network module is simple, it can effectively improve the performance of object detection, and can easily be embedded in many detection models. Our experiments are based on the representative one-stage detection model RetinaNet. In the Common Objects in Context (COCO) dataset, when ResNet50 and ResNet101 are used as backbone networks, the detection performances can be increased by 0.4 and 0.5% AP, respectively. When the DSA module and object confidence task are both applied in RetinaNet, the detection performances based on ResNet50 and ResNet101 can be increased by 1.0 and 1.4% AP, respectively. The experiment results show the effectiveness of the DSA module. [ABSTRACT FROM AUTHOR]

We address in the paper the problem of designing an economic model predictive control (EMPC) algorithm that asymptotically achieves the optimal performance despite the presence of plant-model mismatch. To motivate the problem, we present an example of a continuous stirred tank reactor in which available EMPC and tracking model predictive control (MPC) algorithms do not reach the optimal steady state operation. We propose to use an offset-free disturbance model and to modify the target optimization problem with a correction term that is iteratively computed to enforce the necessary conditions of optimality in the presence of plant-model mismatch. Then, we show how the proposed formulation behaves on the motivating example, highlighting the role of the stage cost function used in the finite horizon MPC problem. [ABSTRACT FROM AUTHOR]

In this paper, a robust fuzzy controller for stochastic nonlinear systems subject to multiple performance constraints is discussed. To solve the problem of stochastic behaviors in nonlinear systems, the covariance control theory and passive control theory are applied based on the concept of energy. Additionally, the pole placement method is considered for the better transient behaviors of the system responses. However, it is known that the control inputs and maximum overshoot of the system responses may become bigger at the same time when the settling time or converge rate of the system is required to be faster. Due to this reason, the input constraint is also considered in the fuzzy controller design method to limit the value of the control gain. Moreover, an effective robust control method is applied to deal with the perturbation of the nonlinear systems. Based on the above performance constraints, the sufficient conditions can be obtained to achieve the stability in the sense of mean square and the multi-performance requirements. Finally, simulation results of the nonlinear synchronous generator system are presented to verify the feasibility and efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

Textile factory water consumption could be optimized to minimize the generation of wastewater, reduce treatment costs, and promote resource recovery. However, downstream plant operation and management is a prime concern in the textile industry, particularly bringing treated wastewater effluent to an acceptable discharge limit. The aim of the study was to optimize key process control parameters to the observed operational challenges of existing processes and suggest an operational guide to the operators and decision makers to enhance the treatment performance in GPS-X. The formulated troubleshooting and decision support strategy, and the optimization results of waste-activated sludge in the primary and secondary clarifiers, was within the range of 15 ± 5 m3/d and 83 ± 7 m3/d, respectively, with a recycle-activated sludge flow of 150 ± 10 m3/d. The sludge retention time was 5 ± 1 d and 6.7 ± 0.5 d in the secondary and primary clarifiers, respectively. The addition of a carbon source in the form of molasses had a flow of 0.5 ± 0.05 m3/d, and the variation in the influent due to wastewater characteristics and rainfall was optimized to 600 ± 50 m3/d. The optimum air flow into the aeration tank was 550 ± 5 m3/hr and saved 91.5% of energy in the optimized process. Thus, the study is indispensable for the effective and efficient operation of the plant and serves as a good guide to the plant operators and decision makers for the best course of action. [ABSTRACT FROM AUTHOR]

A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP. [ABSTRACT FROM AUTHOR]

In this paper, a simple precomputing procedure is proposed to improve the numerical performance of the technological application of critical state soil models. In these models, if associated plasticity is assumed, the normalization of the stress space allows both the yield surface and the plastic components of the elastoplastic matrix to be defined as a function of a single variable. This approach facilitates their parameterization and precomputation, preventing the repetition of calculations when the boundary value problems appear at the yield surface with the calculation of plastic strain. To illustrate the scope of the procedure, its application on a modified Cam Clay model is analysed, which shows that the method allows a significant reduction of about 50% (as compared with the conventional explicit integration algorithm) in the computational time without reducing the precision. Although it is intended for critical state models in soils, the approach can be applied to other materials and types of constitutive models provided that parameterization is possible. It is therefore a methodology of practical interest, especially when a large volume of calculations is required, for example when studying large-scale engineering systems, performing sensitivity analysis, or solving optimization problems. [ABSTRACT FROM AUTHOR]

Constrained optimization problems (COPs) are widely encountered in chemical engineering processes, and are normally defined by complex objective functions with a large number of constraints. Classical optimization methods often fail to solve such problems. In this paper, to solve COPs efficiently, a two-phase search method based on a heat transfer search (HTS) algorithm and a tandem running (TR) strategy is proposed. The main framework of the MHTS–TR method aims to alternate between a feasible search phase that only examines feasible solutions, using the HTS algorithm, and an infeasible search phase where the treatment of infeasible solutions is relaxed in a controlled manner, using the TR strategy. These two phases play different roles in the search process; the former ensures an intensified optimum in a relevant feasible region, whereas the latter is used to introduce more diversity into the former. Thus, the ensemble of these two complementary phases can provide an effective method to solve a wide variety of COPs. The proposed variant was investigated over 24 well-known constrained benchmark functions, and then compared with various well-established metaheuristic approaches. Furthermore, it was applied for solving a chemical COP. The promising results demonstrate that the MHTS–TR approach is applicable for solving real-world COPs. [ABSTRACT FROM AUTHOR]

Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator's structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion. [ABSTRACT FROM AUTHOR]