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Over the past four decades, the prophecy from computer pundits and prognosticators pointed to the looming arrival of the paperless office era. However, forty years later, physical paper documents are still playing a significant role due to the ease of use, superior readability, and availability. The drawbacks of paper sheets are that they are hard to modify and retrieve, have limited space, and are environmentally unfriendly. Augmenting paper documents with digital information from mobile devices extends the two-dimensional space of physical paper documents. Various camera-based recognition and detection devices have been proposed to augment paper documents with digital information. However, there are still some limitations that exist in these systems. This paper presents a novel, low cost, spatially aware, mobile system called Ultrasonic PhoneLens. The Ultrasonic PhoneLens adopts two-dimensional dynamic image presentation and ultrasonic sound positioning techniques. It consists of two ultrasonic sound sensors, one Arduino mini-controller board, and one android mobile device. Based on the location of the mobile device over the physical paper, Ultrasonic PhoneLens can retrieve pre-saved digital information from a mobile database for the object (such as a text, a paragraph, or an image) in a paper document. An empirical study was conducted to evaluate the system performance. The results indicate that our system has a better performance in tasks such as browsing multivalent documents and sharing digital information than the Wiimote PhoneLens system.

UAV trajectory planning is one of the research focuses in artificial intelligence and UAV technology. The asymmetric information, however, will lead to the uncertainty of the UAV trajectory planning, the probability theory as the most commonly used method to solve the trajectory planning problem in uncertain environment will lead to unrealistic conclusions under the condition of lacking samples, while the uncertainty theory based on uncertain measures is an efficient method to solve such problems. Firstly, the uncertainties in trajectory planning are sufficiently considered in this paper, the fuel consumption, concealment and threat degree with uncertain variables are taken as the objective functions, the constraints are analyzed according to the maneuverability, and the uncertain multi-objective trajectory planning (UMOTP) model is established. After that, this paper takes both the long-term benefits and its stability into account, and then, the expected-value and standard-deviation efficient trajectory model is established. What is more, this paper solves the Pareto front of the trajectory planning, satisfying various preferences, which avoids the defects of the trajectory obtained by traditional model only applicable to a certain specific situation. In order to obtain a better solution set, this paper proposes an improved backbones particle swarm optimization algorithm based on PSO and NSGA-II, which overcomes the shortcomings of the traditional algorithm such as premature convergence and poor robustness, and the efficiency of the algorithm is tested. Finally, the algorithm is applied to the UMOTP problem, then, the optimal trajectory set is obtained, and the effectiveness and reliability of the model is verified.

With the development of mobile network technology, the continuous increase of mobile traffic has put forward higher requirements for quality of service (QoS) issues such as asymmetric transmission delay. The paper mainly studies the energy distribution problem on the migration data link from the terminal device to the edge node in the mobile edge network. Multiple data service packages are set up at each hop on the migration data link, and these data service packages compete with each other, and ultimately only one terminal provides and stores energy for this hop. The migration strategy of the data service package is affected by the edge node, and the edge node changes the migration strategy according to the migration strategy of the data service package. The paper is based on the formation of nodes between the data service packages of different nodes on the 5G network data link to jointly control the migration strategy, coordinate the migration strategy formulated, and better coordinate the migration strategy. In this competitive game model, the optimal migration strategy of nodes is found out according to the terminal equipment access requirements. Then according to the node stability rules, the composition of nodes when the nodes are stable is obtained, the migration strategy of stable nodes and the migration and spectrum strategies of operators are obtained, and the migration strategy of joint control provides energy for edge nodes.

Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the lack of infrastructure in many parts of the world. This paper focuses on detecting and classifying diseases present in the leaf images by adopting a hybrid learning model. The proposed hybrid model uses k-means clustering for detecting the disease area from the leaf and a Convolutional Neural Network (CNN) for classifying the type of disease based on comparison between sampled and testing images. The images of leaves under consideration may be symmetrical or asymmetrical in shape. In the proposed methodology, the images of various leaves from diseased plants were first pre-processed to filter out the noise present to get an enhanced image. This improved image enabled detection of minute disease-affected regions. The infected areas were then segmented using k-means clustering algorithm that locates only the infected (diseased) areas by masking the leaves’ green (healthy) regions. The grey level co-occurrence matrix (GLCM) methodology was used to fetch the necessary features from the affected portions. Since the number of fetched features was insufficient, more synthesized features were included, which were then given as input to CNN for training. Finally, the proposed hybrid model was trained and tested using the leaf disease dataset available in the UCI machine learning repository to examine the characteristics between trained and tested images. The hybrid model proposed in this paper can detect and classify different types of diseases affecting different plants with a mean classification accuracy of 92.6%. To illustrate the efficiency of the proposed hybrid model, a comparison was made against the following classification approaches viz., support vector machine, extreme learning machine-based classification, and CNN. The proposed hybrid model was found to be more effective than the other three.

This paper proposes a new methodology to solve multiobjective optimization problems by invoking genetic algorithms and the concept of the Shapley values of cooperative games. It is well known that the Pareto-optimal solutions of multiobjective optimization problems can be obtained by solving the corresponding weighting problems that are formulated by assigning some suitable weights to the objective functions. In this paper, we formulated a cooperative game from the original multiobjective optimization problem by regarding the objective functions as the corresponding players. The payoff function of this formulated cooperative game involves the symmetric concept, which means that the payoff function only depends on the number of players in a coalition and is independent of the role of players in this coalition. In this case, we can reasonably set up the weights as the corresponding Shapley values of this formulated cooperative game. Under these settings, we can obtain the so-called Shapley–Pareto-optimal solution. In order to choose the best Shapley–Pareto-optimal solution, we used genetic algorithms by setting a reasonable fitness function.

Service fulfillment for clients increasingly involves cooperation between information technology (IT) systems. Designing such solutions requires an architectural approach that ensures symmetry between the communicating parties. For the design of such systems, the author introduces the 1+5 architectural views model. The model contains three new architectural views. For business process modeling, it ensures the integrated processes view. Integration aspects cover two additional views: integrated services, and contracts. Moreover, new stereotypes and tagged values have been added to the unified modeling language (UML). The author has introduced two profiles: UML profile for integration flows, and UML profile for distributed ledger deployment. Communication between systems requires flows that arrange mediation mechanisms. The paper describes an integration flow diagram that extends a UML activity diagram. In the case of blockchain, the author has proposed the smart contract design pattern. The paper describes three case studies that have employed the model to design various solutions. The 1+5 model has proven to be well suited for designing both centralized integration environments with enterprise service bus (ESB) and distributed blockchain solutions with peer-to-peer (P2P) connections.

Once a train breaks down on a busy railway line, it will affect the whole traffic network. However, when a rescue locomotive is hooked up to the broken train for towing it to the next station, it is common that coupling dislocation occurs, which results in damages to couplers and the driver’s cab. To ensure the safety of the trains during the coupling, it becomes crucial to determine whether they can be linked safely and automatically under different line conditions. In this paper, position and pose of the rescue locomotive and the broken train on the line are calculated by geometric analytical calculation method, which determines the position relation of their couplers. Then a so-called “coupling characteristic triangle” was proposed to determine whether trains can be safely and automatically linked on the railway line. The triangles are constructed by the peak points of the couplers head of the front vehicle and the rear one and border lines of secure coupling area on the couplers. By judging the shape of the triangle, it can directly judge whether their couplers can be connected successfully. The method has been applied to check the safety of the trains during coupling on the Nanchang urban railway Line 4. The results show that the maximum swing angle of the coupler reaches 17.3957° in the straight–curve section with a radius of 325 m. At this time, coupling is most difficult, and trains need to be connected manually through the tractor, all the calculation results are verified in the actual line. By comparing different calculation methods for judging coupling safety, it is shown that the method proposed in this paper is accurate, efficient, and users can judge coupling safety more intuitively.

The scientific community has presented various citation classification models to refute the concept of pure quantitative citation analysis systems wherein all citations are treated equally. However, a small number of benchmark datasets exist, which makes the asymmetric citation data-driven modeling quite complex. These models classify citations for varying reasons, mostly harnessing metadata and content-based features derived from research papers. Presently, researchers are more inclined toward binary citation classification with the belief that exploiting the datasets of incomplete nature in the best possible way is adequate to address the issue. We argue that contemporary ML citation classification models overlook essential aspects while selecting the appropriate features that hinder elutriating the asymmetric citation data. This study presents a novel binary citation classification model exploiting a list of potential natural language processing (NLP) based features. Machine learning classifiers, including SVM, KLR, and RF, are harnessed to classify citations into important and non-important classes. The evaluation is performed using two benchmark data sets containing a corpus of around 953 paper-citation pairs annotated by the citing authors and domain experts. The study outcomes exhibit that the proposed model outperformed the contemporary approaches by attaining a precision of 0.88.

An improved equilibrium optimizer (EO) algorithm is proposed in this paper to address premature and slow convergence. Firstly, a highly stochastic chaotic mechanism is adopted to initialize the population for range expansion. Secondly, the capability to conduct global search to jump out of local optima is enhanced by assigning adaptive weights and setting adaptive convergence factors. In addition 25 classical benchmark functions are used to validate the algorithm. As revealed by the analysis of the accuracy, speed, and stability of convergence, the IEO algorithm proposed in this paper significantly outperforms other meta-heuristic algorithms. In practice, the distribution is asymmetric because most logging data are unlabeled. Traditional classification models have difficulty in accurately predicting the location of oil layer. In this paper, the oil layers related to oil exploration are predicted using long short-term memory (LSTM) networks. Due to the large amount of data used, however, it is difficult to adjust the parameters. For this reason, an improved equilibrium optimizer algorithm (IEO) is applied to optimize the parameters of LSTM for improved performance, while the effective IEO-LSTM is applied for oil layer prediction. As indicated by the results, the proposed model outperforms the current popular optimization algorithms including particle swarm algorithm PSO and genetic algorithm GA in terms of accuracy, absolute error, root mean square error and mean absolute error.

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we discussed the fitting effect of the symmetrical high formwork monitoring data using the autoregressive–moving-average (ARMA) model and the back propagation neural networks (BPNN) combined model to process. In the actual project, the symmetry of the high formwork system allows the analysis of local monitoring results to be well extended to the whole. For the establishment of the ARMA model, the accurate judgment of the model order has a significant impact. In this paper, back propagation neural networks (BPNN) are used to simulate the ARMA process. The order of the ARMA model is estimated by determining the optimal neural network structure, which is suitable for linear or nonlinear sequences. We validated this approach from the ARMA model data simulated in Monte Carlo and compared it with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The length of the sequence, the coefficients and the order of the ARMA model are considered as factors that influence the judgment effect. Under different conditions, the BPNN always shows an accuracy rate of more than 90%, while the BIC only has a higher accuracy rate when the model order is low and the judgment efficiency of the AIC is below 50%. Finally, the proposed method successfully modeled the stress sequence and obtained the stress change trend. Compared with AIC and BIC, the efficiency of the processing time series is increased by about 50% when an order is obtained by BPNN.

Blockchain networks are based on cryptographic notions that include asymmetric-key encryption, hash functions and consensus protocols. Despite their popularity, mainstream protocols, such as Proof of Work or Proof of Stake still have drawbacks. Efforts to enhance these protocols led to the birth of alternative consensus protocols, catering to specific areas, such as medicine or transportation. These protocols remain relatively unknown despite having unique merits worth investigating. Although past reviews have been published on popular blockchain consensus protocols, they do not include most of these lesser-known protocols. Highlighting these alternative consensus protocols contributes toward the advancement of the state of the art, as they have design features that may be useful to academics, blockchain practitioners and researchers. In this paper, we bridge this gap by providing an overview of alternative consensus protocols proposed within the past 3 years. We evaluate their overall performance based on metrics such as throughput, scalability, security, energy consumption, and finality. In our review, we examine the trade-offs that these consensus protocols have made in their attempts to optimize scalability and performance. To the best of our knowledge, this is the first paper that focuses on these alternative protocols, highlighting their unique features that can be used to develop future consensus protocols.

Deepfake is one of the applications that is deemed harmful. Deepfakes are a sort of image or video manipulation in which a person’s image is changed or swapped with that of another person’s face using artificial neural networks. Deepfake manipulations may be done with a variety of techniques and applications. A quintessential countermeasure against deepfake or face manipulation is deepfake detection method. Most of the existing detection methods perform well under symmetric data distributions, but are still not robust to asymmetric datasets variations and novel deepfake/manipulation types. In this paper, for the identification of fake faces in videos, a new multistream deep learning algorithm is developed, where three streams are merged at the feature level using the fusion layer. After the fusion layer, the fully connected, Softmax, and classification layers are used to classify the data. The pre-trained VGG16 model is adopted for transferred CNN1stream. In transfer learning, the weights of the pre-trained CNN model are further used for training the new classification problem. In the second stream (transferred CNN2), the pre-trained VGG19 model is used. Whereas, in the third stream, the pre-trained ResNet18 model is considered. In this paper, a new large-scale dataset (i.e., World Politicians Deepfake Dataset (WPDD)) is introduced to improve deepfake detection systems. The dataset was created by downloading videos of 20 different politicians from YouTube. Over 320,000 frames were retrieved after dividing the downloaded movie into little sections and extracting the frames. Finally, various manipulations were performed to these frames, resulting in seven separate manipulation classes for men and women. In the experiments, three fake face detection scenarios are investigated. First, fake and real face discrimination is studied. Second, seven face manipulations are performed, including age, beard, face swap, glasses, hair color, hairstyle, smiling, and genuine face discrimination. Third, performance of deepfake detection system under novel type of face manipulation is analyzed. The proposed strategy outperforms the prior existing methods. The calculated performance metrics are over 99%.

DNA computers and quantum computers are gaining attention as alternatives to classical digital computers. DNA is a biological material that can be reprogrammed to perform computing functions. Quantum computing performs reversible computations by nature based on the laws of quantum mechanics. In this paper, DNA computing and reversible computing are combined to propose novel theoretical methods to implement reversible gates and circuits in DNA computers based on strand displacement reactions, since the advantages of reversible logic gates can be exploited to improve the capabilities and functionalities of DNA computers. This paper also proposes a novel universal reversible gate library (URGL) for synthesizing n-bit reversible circuits using DNA to reduce the average length and cost of the constructed circuits when compared with previous methods. Each n-bit URGL contains building blocks to generate all possible permutations of a symmetric group of degree n. Our proposed group (URGL) in the paper is a permutation group. The proposed implementation methods will improve the efficiency of DNA computer computations as the results of DNA implementations are better in terms of quantum cost, DNA cost, and circuit length.

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify the emitter to which the received signals belong. Due to differences in transmitter hardware, this fingerprint cannot be duplicated. Therefore, SEI plays an important role in the field of information security and can reduce the information leakages caused by key theft. This method can also be used in the military field to support communication countermeasures via emitter individual identification. In this paper, empirical mode decomposition is carried out for each radar pulse signal, and then the bispectral features are extracted. Dimensionality reduction is carried out according to the symmetry of the bispectral features. The features after dimensionality reduction are input into a one-dimensional LeNet neural network as the fingerprint features of the emitter, and the identification of 10 radar emitter sources is completed. Based on the verification of real signals, the SEI identification strategy in this paper achieved a recognition rate of 96.4% for 10 radar signals, 98.9% for 10 data emitter signals, and 88.93% for 5 communication radio signals.

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

Forward error correction codes (FEC) are one of the vital sections of modern communication systems, therefore, recognition of the coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, based on information asymmetry, the receiver cannot know the types of channel coding previously used in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. Although the traditional algorithm can also recognize the type of codes, it is only applicable to the case without errors, and its practicability is poor. In the paper, we propose a new method to identify the types of FEC codes based on depth distribution in non-cooperative communication. The proposed algorithm can effectively recognize linear block codes, convolutional codes, and Turbo codes under a low error probability level, and has a higher robustness to noise transmission environment. In addition, an improved matrix estimation algorithm based on Gaussian elimination was adopted in this paper, which effectively improves the parameter identification in a noisy environment. Finally, we used a general framework to unify all the reconstruction algorithms to simplify the complexity of the algorithm. The simulation results show that, compared with the traditional algorithm based on matrix rank, the proposed algorithm has a better anti-interference performance. The method proposed is simple and convenient for engineering and practical applications.

In this paper, we develop two algorithms to solve a nonlinear system of symmetric equations. The first is an algorithm based on modifying two Broyden–Fletcher–Goldfarb–Shanno (BFGS) methods. One of its advantages is that it is more suitable to effectively solve a small-scale system of nonlinear symmetric equations. In contrast, the second algorithm chooses new search directions by incorporating an approximation method of computing the gradients and their difference into the determination of search directions in the first algorithm. In essence, the second one can be viewed as an extension of the conjugate gradient method recently proposed by Lv et al. for solving unconstrained optimization problems. It was proved that these search directions are sufficiently descending for the approximate residual square of the equations, independent of the used line search rules. Global convergence of the two algorithms is established under mild assumptions. To test the algorithms, they are used to solve a number of benchmark test problems. Numerical results indicate that the developed algorithms in this paper outperform the other similar algorithms available in the literature.

Text, voice, images and videos can express some intentions and facts in daily life. By understanding these contents, people can identify and analyze some behaviors. This paper focuses on the commodity trade declaration process and identifies the commodity categories based on text information on customs declarations. Although the technology of text recognition is mature in many application fields, there are few studies on the classification and recognition of customs declaration goods. In this paper, we proposed a classification framework based on machine learning (ML) models for commodity trade declaration that reaches a high rate of accuracy. This paper also proposed a symmetrical decision fusion method for this task based on convolutional neural network (CNN) and transformer. The experimental results show that the fusion model can make up for the shortcomings of the two original models and some improvements have been made. In the two datasets used in this paper, the accuracy can reach 88% and 99%, respectively. To promote the development of study of customs declaration business and Chinese text recognition, we also exposed the proprietary datasets used in this study.

As a further extension of the fuzzy set and the intuitive fuzzy set, the interval-valued intuitive fuzzy set (IIFS) is a more effective tool to deal with uncertain problems. However, the classical rough set is based on the equivalence relation, which do not apply to the IIFS. In this paper, we combine the IIFS with the ordered information system to obtain the interval-valued intuitive fuzzy ordered information system (IIFOIS). On this basis, three types of multiple granulation rough set models based on the dominance relation are established to effectively overcome the limitation mentioned above, which belongs to the interdisciplinary subject of information theory in mathematics and pattern recognition. First, for an IIFOIS, we put forward a multiple granulation rough set (MGRS) model from two completely symmetry positions, which are optimistic and pessimistic, respectively. Furthermore, we discuss the approximation representation and a few essential characteristics for the target concept, besides several significant rough measures about two kinds of MGRS symmetry models are discussed. Furthermore, a more general MGRS model named the generalized MGRS (GMGRS) model is proposed in an IIFOIS, and some important properties and rough measures are also investigated. Finally, the relationships and differences between the single granulation rough set and the three types of MGRS are discussed carefully by comparing the rough measures between them in an IIFOIS. In order to better utilize the theory to realistic problems, an actual case shows the methods of MGRS models in an IIFOIS is given in this paper.

Cross-efficiency evaluation effectively distinguishes a set of decision-making units (DMUs) via self- and peer-evaluations. In constant returns to scale, this evaluation technique is usually applied for data envelopment analysis (DEA) models because negative efficiencies will not occur in this case. For situations of variable returns to scale, the negative cross-efficiencies may occur in this evaluation method. In the real world, the observations could be uncertain and difficult to measure precisely. The existing fuzzy cross-evaluation methods are restricted to production technologies with constant returns to scale. Generally, symmetry is a fundamental characteristic of binary relations used when modeling optimization problems. Additionally, the notion of symmetry appeared in many studies about uncertain theories employed in DEA problems, and this approach can be considered an engineering tool for supporting decision-making. This paper proposes a fuzzy cross-efficiency evaluation model with fuzzy observations under variable returns to scale. Since all possible weights of all DMUs are considered, a choice of weights is not required. Most importantly, negative cross-efficiencies are not produced. An example shows that this paper’s fuzzy cross-efficiency evaluation method has discriminative power in ranking the DMUs when observations are fuzzy numbers.

Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms.

The openness of the environment brings great challenges to the swarm robotic system to cover the task area quickly and effectively. In this paper, a coverage method based on gradient and grouping (GGC) is proposed. What is novel about our proposed solution is that it is suitable for extremely simple robots that lack computing or storage power. Through the change of the robot gradient, the swarm robot system with very simple functions can effectively self-organize to cover the unknown task area. By grouping the swarm robots, each group can cover the task area in parallel, which greatly improves the coverage speed. We verified our proposed method through experimental simulation and found that the gradient and grouping-based method in this paper was superior to other methods in terms of coverage, coverage completion time, and other aspects. Simultaneously, the robustness of the proposed method is analyzed and admirable experimental results are obtained. Because the applicable robot is very simple, the method in this paper can be applied to the submillimeter swarm robot system, which will lay the foundation for micro medicine.

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

The complexity of a mathematical expression is a measure that can be used to compare the expression with other mathematical expressions and judge which one is simpler. In the paper, we analyze three effect factors for the complexity of a mathematical expression: representational length, computational time, and intelligibility. Mainly, the paper introduces a binary-lambda-calculus based calculation method for representational complexity and a rule based calculation method for algebraic computation complexity. In the process of calculating the representation complexity of mathematical expressions, we transform the de bruijn notation into the binary lambda calculus of mathematical expressions that is inspired by compressing symmetry strings in Kolmogorov complexity theorem. Furthermore, the application of complexity of mathematical expressions in MACP, a mathematics answer checking protocol, is also addressed. MACP can be used in a computer aided assessment system in order to compute correct answers, verify equivalence of expressions, check user answers whether in a simplification form, and give automatic partial grades.

The picture fuzzy set is a generation of an intuitionistic fuzzy set. The aggregation operators are important tools in the process of information aggregation. Some aggregation operators for picture fuzzy sets have been proposed in previous papers, but some of them are defective for picture fuzzy multi-attribute decision making. In this paper, we introduce a transformation method for a picture fuzzy number and trapezoidal fuzzy number. Based on this method, we proposed a picture fuzzy multiplication operation and a picture fuzzy power operation. Moreover, we develop the picture fuzzy weighted geometric (PFWG) aggregation operator, the picture fuzzy ordered weighted geometric (PFOWG) aggregation operator and the picture fuzzy hybrid geometric (PFHG) aggregation operator. The related properties are also studied. Finally, we apply the proposed aggregation operators to multi-attribute decision making and pattern recognition.

The substitution box (S-box) is the only nonlinear components in the symmetric block cipher. Its performance directly determines the security strength of the block cipher. With the dynamic characteristics degradation and the local periodic phenomenon of digital chaos, and the security problems caused by them becoming more and more prominent, how to efficiently generate an S-box with security guarantee based on chaos has gradually attracted the attention of cryptographers. In this paper, a chaotic S-box construction method is proposed based on a memorable simulated annealing algorithm (MSAA). The chaotic S-box set is produced by using the nonlinearity and randomness of the dynamic iteration of digital cascaded chaotic mapping. The composite objective function is constructed based on the analysis of the performance indexes of S-box. The MSAA is used to efficiently optimize the S-box set. The matrix segmentation and scrambling operations are carried out on the optimized S-box. The cryptographic performance of chaotic S-box is tested and analyzed, and compared with the mainstream chaotic S-box of the same kind. The results show that the S-box constructed in this paper can not only stably and efficiently generate chaotic S-box with better performance, but also make an effective exploration of the construction of chaotic S-boxes based on intelligent algorithms.

This paper analyzes the representation behaviors of a comparison measure between two compared fuzzy sets. Three types of restrictions on two fuzzy sets are considered in this paper: two disjoint union fuzzy sets, two disjoint fuzzy sets and two general fuzzy sets. Differences exist among the numbers of possible representations of a comparison measure for the three types of fuzzy sets restrictions. The value of comparison measure is constant for two disjoint union fuzzy sets. There are 49 candidate representations of a comparison measure for two disjoint fuzzy sets, of which 13 candidate representations with one or two terms are obtained. For each candidate representation, a variant of the general axiomatic definition for a comparison measure is presented. Choosing the right candidate representation for a given application, we can easily and efficiently calculate and compare a comparison measure.

The protection of information processed electronically involves a large number of IT devices from computer sets or laptops to monitors, printers, servers, etc. In many cases, classified information processing might be associated with the use of projectors, which are an indispensable element of meetings for a limited group of people. Such devices are connected to computers through interfaces of various analogue and digital standards and can become an additional source of unwanted emissions, and the distinctive features of these emissions allow the information displayed to be unwantedly reproduced. This paper offers evidence of the existing threat related to electromagnetic infiltration of several projectors, by showing images reconstructed from registered revealing emissions. The paper presents an analysis of several solutions that can be used to reduce the level of infiltration susceptibility of projectors or to highlight this property in the device assessment process. The possibilities of using special computer fonts and the so-called method of colors—background color and text color—is analyzed. The tests were carried out on randomly selected projectors in two independent laboratories, and, based on these results, a number of interesting conclusions have been highlighted at the end.

The property of nonlinearity has high importance for the design of strong substitution boxes. Therefore, the development of new techniques to produce substitution boxes with high values of nonlinearity is essential. Many research papers have shown that optimization algorithms are an efficient technique to obtain good solutions. However, there is no reference in the public literature showing that a heuristic method obtains optimal nonlinearity unless seeded with optimal initial solutions. Moreover, the majority of papers with the best nonlinearity reported for pseudo-random seeding of the algorithm(s) often achieve their results with the help of some cost function(s) over the Walsh–Hadamard spectrum of the substitution. In the sense, we proposed to present, in this paper, a novel external parameter independent cost function for evolving bijective s-boxes of high nonlinearity, which is highly correlated to this property. Several heuristic approaches including GaT (genetic and tree), LSA (local search algorithm), and the Hill Climbing algorithm have been investigated to assess the performance of evolved s-boxes. A performance comparison has been done to show the advantages of our new cost function, with respect to cost functions for s-boxes like Clark’s and Picek’s cost functions.

In this paper, we propose a novel kinematic and inverse dynamic model for the flybar-less (FBL) swashplate mechanism of a small-scale unmanned helicopter. The swashplate mechanism is an essential configuration of helicopter flight control systems. It is a complex, multi-loop chain mechanism that controls the main rotor. In recent years, the demand for compact swashplate designs has increased owing to the development of small-scale helicopters. The swashplate mechanism proposed in this paper is the latest architectures used for hingeless rotors without a Bell-Hiller mixer. Firstly, the kinematic analysis is derived from the parallel manipulators concepts. Then, based on the principle of virtual work, a methodology for deriving a closed-form dynamic equation of the FBL swashplate mechanism is developed. Finally, the correctness and efficiency of the presented analytical model are demonstrated by numerical examples and the influence factors of the loads acted on actuators are discussed.

Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.

The development of web applications essentially relies on users’ demands who expect highly efficacious but cost effective software services. Managing time and cost so as to develop web applications that cater to the users’ need is a challenge for developers at present. Moreover, low-cost maintenance can only be achieved by enhancing the durability of the web applications. Identifying characteristics of durability is a complex task because the different experts have different opinions regarding the significance of characteristics that determine durability quotient of the web applications. As established by the best practices undertaken in this context, some experts consider quality to be the most important factor for determining durability. Therefore, the present study enlists multi-criteria decision-based symmetrical technique to address the multi-vector option availability for the apt selection of the characteristics for durability. Furthermore, it has also been identified that a numerical assessment of web applications’ durability can affect the service life and low-cost management in web applications. In this context, to achieve high durability and longevity in web applications, this paper attempts to illustrate and perform a numerical evaluation of durability characteristics. By understanding the various characteristics and their significance towards durability, the paper finds that the hesitant fuzzy-based symmetrical technique of the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an effective methodology for evaluating web applications’ durability. For evaluating the quality of the results and establishing their sensitivity, the authors have assessed the outcomes on six different projects of the University. Additionally, results assessed and discussed in this paper would be a conclusive reference for the developers in their attempts to develop highly durable and manageable web applications.

This paper presents a modeling and simulation approach in order to perform a generative analysis of folktales aimed at validating Claude L&eacute, vi-Strauss&rsquo, theory and method. To this aim, a discrete-event simulation is proposed. The simulation is based on the development of a set of discrete-event models dedicated to generating a set of folktales from an initial one, according to Claude L&eacute, structural analysis based on symmetry and double twist transformations. This paper describes in detail how these discrete-event models have been implemented in the framework of the DEVSimPy software environment by using myths of Native American mythology and folktales of Corsican oral literature. The validation involved the following steps: (i) definition of a reference folktale (according to Claude L&eacute, methodology) (ii) generation of a set of folktales by performing their own transformations (iii) generation of a graph allowing to analyze the links that have been created after performing a set of folktales transformations. Finally, the computational validation of L&eacute, s method is intended to ground a new research that may reformulate structural analysis and elaborate a neo-structural model of canonical formalization based on transformational morphodynamics. The aim is to conceptualize and measure recursively the structural dynamics and the recurrent patterns of current identity transformations in liberal democracies, especially in US and EU contexts where ethnic/racial divisions and migration challenges are becoming more acute than ever.

Many scientific papers are devoted to solving multi-criteria problems. Researchers solve these problems, usually using methods that find discrete solutions and with the collaboration of domain experts. In both symmetrical and asymmetrical problems, the challenge is when new decision-making variants emerge. Unfortunately, discreet identification of preferences makes it impossible to determine the preferences for new alternatives. In this work, we propose a new approach to identifying a multi-criteria decision model to address this challenge. Our proposal is based on stochastic optimization techniques and the characteristic objects method (COMET). An extensive work comparing the use of hill-climbing, simulated annealing, and particle swarm optimization algorithms are presented in this paper. The paper also contains preliminary studies on initial conditions. Finally, our approach has been demonstrated using a simple numerical example.

Data from sensor-bearing satellites requires processing aboard terrestrial data centres that use water for cooling at the expense of high data-transfer latency. The reliance of terrestrial data centres on water increases their water footprint and limits the availability of water for other applications. Therefore, data centres with low data-transfer latency and reduced reliance on Earth&rsquo, s water resources are required. This paper proposes space habitat data centres (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by 11.9&ndash, 33.6% and 46.7&ndash, 77% on average, respectively.

Recently, Li et al. proposed a data hiding method based on pixel value ordering (PVO) and prediction error expansion (PEE). In their method, maximum and minimum values were predicted and the pixel values were modified to embed secret data. Thereafter, many scholars have proposed improvisations to the original PVO method. In this paper, a Reversible data hiding (RDH) method is proposed where the secret data is dispersed into two layers using different modes of operations. The second layer changes the dividing mode, and the first and the second layers do not take duplicate blocks. Under a fixed embedding capacity, threshold value and block size are controlled, complex blocks are filtered and the secret data is hidden in smooth blocks. This paper also compares the effectiveness of four well-known PVO series methods, the latest PVO methods, difference expansion (DE) method and reduced difference expansion (RDE) method. Experimental results show that the proposed method reduces distortion in the image, thereby enhancing the visual symmetry/quality compared to previous state-of-the-art methods and increasing its high application value.

Along with the fourth industrial revolution, the use of unmanned aerial vehicles (UAV) has grown very rapidly over the past decade. With this rapid growth, studies using UAVs are underway in various areas. UAVs are more economical and effective when utilizing several nodes rather than operating a single aircraft. In general, UAVs collect and transmit information to the control center (CC), and act on control commands from the CC. Communication between UAVs and the control center is usually achieved using modules such as radio frequency (RF), Bluetooth, Wireless Fidelity (Wi-Fi) and cellular. However, when multiple UAVs communicate directly with the CC, the limitations of communication technologies and problems with increasing nodes occur. To address these points, several studies have constructed an ad-hoc network of UAVs to address the limitations of Wi-Fi and Bluetooth communication range, or the high cost of cellular systems. However, previous studies have constructed fixed topology ad-hoc networks. These studies did not take into account the problem of changing network topology due to the rapid mobility and frequent formation changes of UAVs. Due to this, limits occurred such that UAVs moved only in the pre-built topology. In this paper, we propose a dynamic topology construction protocol for UAV swarms to address this problem. The main contents of this paper are as follows. We first look at the research and limitations of existing UAV communications, and propose a protocol to solve this problem. This paper proposes a protocol for UAV construction of ad-hoc networks when trying to perform missions using multiple UAVs, and also describes how the changed network topology is reconstructed when network topology changes due to changes in flight formation. Finally, we establish a situation and apply the proposed protocol, analyze the results and describe further required research.

One of the key performance requirements for different control systems is non-overshooting step response, so that the controllable value should not overcome the reference value within a transient process. The problem of providing a non-overshooting step response was examined in this paper. Despite much scientific research being dedicated to the overshoot elimination problem, there are little to no results regarding parametric uncertainty for the discussed problem. Consideration of parametric uncertainty, particularly in the form of interval-given parameters, is essential, since in many physical processes, electronic devices and control systems parameter values can be obtained with acceptable error, and they can vary under different conditions. The main result of our research is the development of a proportional-integral-derivative (PID)-controller tuning approach for systems with interval-given parameters that provides a non-overshooting step response for such classes of control systems. This paper investigates analytical conditions and constraints for linear time invariant (LTI) systems in order to have no overshoot, enhances them with respect to parametric uncertainty, and formulates rules for tuning choices of parameters.

The paper describes the substance image formation based on the measurements by multisensor systems and the possibility of the development of a gas analysis device like an electronic nose. Classification of gas sensors and the need for their application for the recognition of difficult images of multicomponent air environments are considered. The image is formed based on stochastic transformations, calculations of correlation, and fractal dimensions of reconstruction attractors. The paper shows images created for substances with various structures that were received with the help of a multisensor system under fixed measurement conditions.

The fluctuation of the stock market has a symmetrical characteristic. To improve the performance of self-forecasting, it is crucial to summarize and accurately express internal fluctuation rules from the historical time series dataset. However, due to the influence of external interference factors, these internal rules are difficult to express by traditional mathematical models. In this paper, a novel forecasting model is proposed based on probabilistic linguistic logical relationships generated from historical time series dataset. The proposed model introduces linguistic variables with positive and negative symmetrical judgements to represent the direction of stock market fluctuation. Meanwhile, daily fluctuation trends of a stock market are represented by a probabilistic linguistic term set, which consist of daily status and its recent historical statuses. First, historical time series of a stock market is transformed into a fluctuation time series (FTS) by the first-order difference transformation. Then, a fuzzy linguistic variable is employed to represent each value in the fluctuation time series, according to predefined intervals. Next, left hand sides of fuzzy logical relationships between currents and their corresponding histories can be expressed by probabilistic linguistic term sets and similar ones can be grouped to generate probabilistic linguistic logical relationships. Lastly, based on the probabilistic linguistic term set expression of the current status and the corresponding historical statuses, distance measurement is employed to find the most proper probabilistic linguistic logical relationship for future forecasting. For the convenience of comparing the prediction performance of the model from the perspective of accuracy, this paper takes the closing price dataset of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as an example. Compared with the prediction results of previous studies, the proposed model has the advantages of stable prediction performance, simple model design, and an easy to understand platform. In order to test the performance of the model for other datasets, we use the prediction of the Shanghai Stock Exchange Composite Index (SHSECI) to prove its universality.

This paper analyzed the locomotion of a snake robot in narrow spaces such as a pipe or channel. We developed a unique experimental snake robot with one revolute and one linear joint on each module, with the ability to perform planar motion. The designed locomotion pattern was simulated in MATLAB R2015b and subsequently verified by the experimental snake robot. The locomotion of the developed snake robot was also experimentally analyzed on dry and viscous surfaces. The paper further describes the investigation of locomotion stability by three symmetrical curves used to anchor static modules between the walls of the pipe. The stability was experimentally analyzed by digital image correlation using a Q-450 Dantec Dynamics high-speed correlation system. The paper presents some input symmetrical elements of locomotion and describes their influence on the results of locomotion. The results of simulations and experiments show possibilities of snake robot locomotion in a pipe. Web of Science 12 6 art. no. 939

The analysis of operations of the passenger traffic operator in the Republic of Srpska (RS) showed that the volume of passenger transport has, for the last fifteen years, been in constant decline. It is of particular importance that the operator has, year after year, recorded a negative balance of business. The way out of the current unfavorable situation in the sector of passenger traffic is based on the application of Public Service Obligation (PSO) based on the Regulation 1370/2007. In order to solve the problems, seven realistically possible variants have been identified. This paper defines the criteria for selecting the best variant, as well as a new integrated fuzzy model for the selection of the best variant that will enable the operator to make a profit. To define the weights of criteria in this paper, we have used the fuzzy PIvot Pairwise RElative Criteria Importance Assessment (F-PIPRECIA) method, while for ranking and selection of the best variant, we have used the Fuzzy Evaluation based on Distance from Average Solution (F-EDAS) method. Results show that the seventh variant: “Increase in revenue from ticket sales and PSO services and reduction in costs“ is the best solution in current conditions. Validation tests are performed with different scenarios and approaches and show that the model is stable. A validity test was created consisting of variations in the significance of model input parameters, testing of reverse rank, applying the fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (F-MARCOS), fuzzy Simple Additive Weighing (F-SAW) method, and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). As a part of the validation tests, Spearman’s coefficient of correlation (SCC) in some scenarios is performed and weights of the criteria have been obtained using the Fuzzy Analytic Hierarchy Process (F-AHP) and Full Consistency Method (FUCOM).

As an emerging technique of wireless communication, visible light communication is experiencing a boom in the global communications field, and the dream of accessing the Internet with light is fast becoming a reality. Multiple input multiple output, which is a key technique in radio frequency communication, can multiply channel capacity. However, it suffers the trouble of too large channel correlation when directly applied to visible light communication. The aims of this paper were to investigate spatial modulation and cooperative communication, then put forward a cooperative strategy based on space–time labeling diversity for indoor visible light communication systems to achieve high reliability. This scheme was conceived in two steps: (1) a cooperative indoor visible light communication system with a source luminaire, a relay luminaire, and a destination receiver was set up by employing the idea of cooperative transmission. Relative to the destination receiver, these two luminaires can be symmetric or asymmetric in geographical distribution. (2) Space–time labeling diversity of the constellations at the source luminaire was re-adjusted at the relay luminaire to enlarge the product of the distance of corresponding points on both constellations and was introduced on the basis of spatial modulation. Furthermore, total bit error ratio of the proposed cooperative indoor visible light communication system was derived. This scheme was implemented through Monte Carlo simulation. Evaluations of performance demonstrated the superiority of the cooperative strategy based on space–time labeling diversity over conventional optical transmission schemes. The presented approach in this paper could be of some value and interest to those who are working on visible light communication devices.

Growing concern about healthcare information security in the wake of alarmingly rising cyber-attacks is being given symmetrical priority by current researchers and cyber security experts. Intruders are penetrating symmetrical mechanisms of healthcare information security continuously. In the same league, the paper presents an overview on the current situation of healthcare information and presents a layered model of healthcare information management in organizations. The paper also evaluates the various factors that have a key contribution in healthcare information security breaches through a hybrid fuzzy-based symmetrical methodology of AHP-TOPSIS. Furthermore, for assessing the effect of the calculated results, the authors have tested the results on local hospital software of Varanasi. Tested results of the factors are validated through the comparison and sensitivity analysis in this study. Tabulated results of the proposed study propose a symmetrical mechanism as the most conversant technique which can be employed by the experts and researchers for preparing security guidelines and strategies.

In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to achieve confrontation decision-making of multi-agent. In the process of training, the information of other agents is introduced to the critic network to improve the strategy of confrontation. The parameter sharing mechanism can reduce the loss of experience storage. In the DDPG algorithm, we use four neural networks to generate real-time action and Q-value function respectively and use a momentum mechanism to optimize the training process to accelerate the convergence rate for the neural network. Secondly, this paper introduces an auxiliary controller using a policy-based reinforcement learning (RL) method to achieve the assistant decision-making for the game agent. In addition, an effective reward function is used to help agents balance losses of enemies and our side. Furthermore, this paper also uses the knowledge transfer method to extend the learning model to more complex scenes and improve the generalization of the proposed confrontation model. Two confrontation decision-making experiments are designed to verify the effectiveness of the proposed method. In a small-scale task scenario, the trained agent can successfully learn to fight with the competitors and achieve a good winning rate. For large-scale confrontation scenarios, the knowledge transfer method can gradually improve the decision-making level of the learning agent.

In modern recommender systems, matrix factorization has been widely used to decompose the user&ndash, item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user&ndash, item interaction matrix and the item&ndash, item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.

To certain degree, multi-objective optimization problems obey the law of symmetry, for instance, the minimum of one objective function corresponds to the maximum of another objective. To provide effective support for the multi-objective operation of the aerospace product shell production line, this paper studies multi-objective aerospace shell production scheduling problems. Firstly, a multi-objective optimization model for the production scheduling of aerospace product shell production lines is established. In the presented model, the maximum completion time and the cost of production line construction are optimized simultaneously. Secondly, to tackle the characteristics of discreteness, non-convexity and strong NP difficulty of the multi-objective problem, a knowledge-driven multi-objective evolutionary algorithm is designed to solve the problem. In the proposed approach, structural features of the scheduling plan are extracted during the optimization process and used to guide the subsequent optimization process. Finally, a set of test instances is generated to illustrate the addressed problem and test the proposed approach. The experimental results show that the knowledge-driven multi-objective evolutionary algorithm designed in this paper has better performance than the two classic multi-objective optimization methods.

A low-light image enhancement method based on a deep symmetric encoder&ndash, decoder convolutional network (LLED-Net) is proposed in the paper. In surveillance and tactical reconnaissance, collecting visual information from a dynamic environment and accurately processing that data is critical to making the right decisions and ensuring mission success. However, due to the cost and technical limitations of camera sensors, it is difficult to capture clear images or videos in low-light conditions. In this paper, a special encoder&ndash, decoder convolution network is designed to utilize multi-scale feature maps and join jump connections to avoid gradient disappearance. In order to preserve the image texture as much as possible, by using structural similarity (SSIM) loss to train the model on the data sets with different brightness level, the model can adaptively enhance low-light images in low-light environments. The results show that the proposed algorithm provides significant improvements in quantitative comparison with RED-Net and several other representative image enhancement algorithms.