Traditional Problems Research Articles

Multi-level guided evolution algorithm for solving fuzzy flexible job shop problem

Traditional flexible job shop scheduling problems (FJSP) are mostly limited to deterministic environments. In actual production, some uncertain factors lead to changes in job processing time. When solving multi-objective fuzzy flexible job shop scheduling problems (MOFFJSP), the existing evolutionary algorithms do not fully utilize information transfer between levels during the population evolution process. Therefore, a multi-level guided evolutionary (MLGE) optimization method is proposed to solve MOFFJSP. In the proposed MLGE method, decomposition and dominance techniques are combined to balance the diversity and convergence performance of the algorithm. Meanwhile, the idea of the Jaya algorithm approaching good individuals and distancing poor individuals is introduced. Combined with improved Jaya operator operations, it is used to guide individual evolution, preserving and inheriting solutions that perform well in terms of convergence and diversity. Finally, numerous experiments are carried out to evaluate the effectiveness of MLGE. The results show that MLGE can provide promising results for MOFFJSP. Additionally, it shows how MLGE outperforms other comparison algorithms in terms of convergence and variety of solutions.

Exploration and practice of cleaner production of titanium dioxide by sulfuric acid process

In China, titanium dioxide production is still based on sulfuric acid process which suffers from high energy consumption, serious pollution and long process. For solves the traditional problems, cleaner production is an effective way for the high-quality development and comprehensive green transformation of sulfate titanium dioxide industry. Taking a titanium dioxide company in Panzhihua as an example, this paper expounds the exploration and practice of utilizing advanced process equipment to transform the original specific process and improve the clean production process and equipment. The company has upgraded the continuous acidolysis process, VCE (Vapor-Condense-Evaporate) crystallization process and MVR (Mechanical Vapor Recompression) concentration process for the three core sections with serious waste and energy consumption, and effectively coupled with the other original sections. Through the analysis of production operation, the new process significantly improves the comprehensive resources utilization efficiency of titanium, sulfur and iron, improves the working environment of labors, and reduces the energy consumption, the amount of three wastes and the production cost. Company in the gradual realization of cleaner production, made beneficial exploration.

Eco‐friendly glass wet etching for MEMS application: A review

AbstractGlass is one of the most essential materials in various industrial fields. A representative application is as interposers in the semiconductor and display industries and microfluidic devices in the bio‐industry. Due to technological advancements, electric devices and various products are becoming lighter and smaller. Consequently, precision processing of the substrate is becoming increasingly important. One significant leading technology among these is through glass via (TGV) technology, which is attracting attention as a future alternative to through silicon via. In particular, TGV should be capable of microfabrication with a large aspect ratio and a consistent small hole size. TGV is typically fabricated using a wet etching process, so wet etching technology is a prominent focus in microfabrication. However, glass has isotropic properties, making achieving a high aspect ratio difficult. Traditionally, research on the wet etching method using hydrofluoric acid (HF) has been conducted. Nevertheless, HF etching has several disadvantages, including air pollution and human harm. Additional studies have been performed to address these issues. This review paper will cover the conventional HF‐based wet etching method and alternative HF etching processes (eco‐friendly materials). This technology would significantly help overcome the traditional problems associated with HF etching and fabricating precision‐processed glass in the semiconductor, display, and bio‐device industries.

Advancing IOT Security: A Systemic and Cognitive Framework for Military Application

In the coming years, the protection of data, objects, networks, systems, and individuals within the Internet of Things (IoT) will take center stage in research and standardization efforts. The extensive connectivity of smart objects, coupled with their significant limitations, gives rise to numerous security issues that are not addressed by traditional security problem formulations and solutions. This work presents an overview of an IoT security roadmap to guide researchers interested in this field, basedona unique systemic andcognitive perspective. The function of each element of this approach will be clarified, and its relationships with other components and their influence on the entire system will be elaborated. In line with the innovative classification of IoT vision, a case study involving a military live simulation will be showcased to underline the components and interactions of the systemic and cognitive method. Following this, a discussionon security concerns related to privacy, trust, identification, and access control will be conducted, and various research challenges will be underscored.

Distributed sequential optimal power flow under uncertainty in power distribution systems: A data-driven approach

Modern distribution systems with high penetration of distributed energy resources face multiple sources of uncertainty. This transforms the traditional Optimal Power Flow problem into a problem of sequential decision-making under uncertainty. In this framework, the solution concept takes the form of a policy, i.e., a method of making dispatch decisions when presented with a real-time system state. Reasoning over the future uncertainty realization and the optimal online dispatch decisions is especially challenging when the number of resources increases and only a small dataset is available for the system’s random variables. In this paper, we present a data-driven distributed policy for making dispatch decisions online and under uncertainty. The policy is assisted by a Graph Neural Network but is constructed in such a way that the resulting dispatch is guaranteed to satisfy the system’s constraints. The proposed policy is experimentally shown to achieve a performance close to the optimal-in-hindsight solution, significantly outperforming state-of-the-art policies based on stochastic programming and plain machine-learning approaches.

Solving pick-up and delivery problems via deep reinforcement learning based symmetric neural optimization

Recently, there has been a growing trend to use deep reinforcement learning (DRL) to solve NP-hard combinatorial optimization problems such as routing problem, where a policy learned by a deep neural network guides the sequential construction of solutions. Despite their success, most of the previous studies omit the symmetry between the partitions of instances and between solutions, which makes them less effective in handling the more practical scenarios with symmetric features, such as pick-up and delivery problems (PDPs). PDPs are more challenging and practical than traditional routing problems because the correlation between pick-up nodes and delivery nodes in the former is multiple one-to-one and not simple as the correlation in the latter. Besides, most DRL-based methods cannot take both computational cost and the quality of solutions into account. To resolve this issue, we propose the Symmetric Neural Optimization for Pick-up and Delivery Problems (PD-SNO) to solve the pick-up and delivery traveling salesman problem (PDTSP) and the multi-commodity one-to-one pick-up and delivery traveling salesman problem (m-PDTSP), respectively. The PD-SNO leverages the symmetries between different partitions of instances to effectively capture the relationship between symmetric node sets (i.e., pick-up nodes and delivery nodes); the PD-SNO also leverages the symmetry between different trajectories of solution to guide the construction of solutions and training of policy. We evaluate the performance of our PD-SNO, and the results show that it outperforms various representative baselines, including traditional methods (both exact and heuristic algorithms) and state-of-the-art DRL-based methods (both construction and improvement types) on synthetic datasets. Then, we further assess the generalization performance of our PD-SNO on benchmark datasets. Finally, we research the independent effectiveness of our designs through ablation studies.

Distributed state-of-charge and power balance estimation for aggregated battery energy storage systems with EV aggregators

Aggregated battery energy storage systems (ABESSs) play an important role in smart grids. This study considers distributed ABESSs containing electric vehicle (EV) aggregators and battery energy storage systems (BESSs). Due to randomness and uncertainty caused by EV aggregators, the distributed ABESSs face heterogeneous random communication failures between the EV aggregators and the BESSs. Aiming to solve this problem, this study proposes a distributed state-of-charge (SoC) and power balance estimation strategy for ABESSs with an event-triggered mechanism. Different from the traditional SoC balance problem definition in the BESSs, this study introduces an innovative balance convergence condition to solve the problem of power imbalance between the EV aggregators and the BESS units. In addition, an adaptively robust estimation algorithm is designed to estimate the precise value of SoC and power under heterogeneous random failures caused by EV aggregators. Further, a distributed periodic dynamic event-triggered mechanism is developed considering redundant messages in the ABESS communication channels. This mechanism can both handle the Zeno phenomenon and obtain a larger minimum time interval with fewer sent measurements. The simulation results demonstrate the effectiveness of the proposed distributed approach in both the discharging and charging modes of ABESSs.

Optimal control of linear cost networks

We present a method for optimal control with respect to a linear cost function for positive linear systems with coupled input constraints. We show that the Bellman equation giving the optimal cost function and resulting sparse state feedback for these systems can be stated explicitly, with the solution given by a linear program. Our framework admits a range of network routing problems with underlying linear dynamics. These dynamics can be used to model traditional graph-theoretical problems like shortest path as a special case, but can also capture more complex behaviors. We provide an asynchronous and distributed value iteration algorithm for obtaining the optimal cost function and control law.

Perilaku Politik Masyarakat Suku Jawa dalam Pemilihan Kepala Desa (Studi Kasus Desa Bandar Klippa Kecamatan Percut Sei Tuan)

This research explores the political behavior of the Javanese community in Bandar Klippa Village in the village head election, especially the Javanese community but the reality of the problem is still very thick with the problem of Javanese cultural traditions, this still dominates as a national culture and tradition in Indonesia. The election of the village head in Bandar Klippa Village reflects the dynamics of the political behavior of the Javanese community which is influenced by factors that influence the political behavior of the Javanese community such as mutual cooperation and togetherness, traditional customs and values, community economic conditions, attractiveness of economic programs, level of education, criticism and rationalism, influence of community leaders and local elites, family influence, access and influence of social media, campaigns through traditional media, community views on community political behavior in the election of the village head. So that this research uses qualitative which is intended to describe the phenomenon of what is experienced by the research subject in the form of perceptions, actions and behavior which are then described in the form of descriptions, and the techniques used in qualitative research using interviews to obtain sources (informants) from the community around Bandar Klippa.

Configuring Socio-Environmental Risks in Chile: Institutional Narratives and Complexities in a Risk Society

Living in a society characterized by risk has distinct implications for developing countries, where addressing the socio-environmental issues within established governance structures is challenging. This study aims to investigate the risks identified by the main relevant institutions in Chile based on the narratives of their authorities and how these are configured differently according to the regions and areas involved. Using a qualitative method based on individual interviews conducted with 57 representatives of institutions present in eight regions of the country, a differentiated configuration is identified depending on the institution and the region where they perform their functions. The main findings show that (1) the narrative relevance of the risks in Chile continues to be intensely based on natural hazards, (2) there are clear regional and macro-regional differences in the configuration of socio-environmental risks, showing significant institutional complexity, and (3) the ongoing challenges with deregulated events create high-level uncertainty in matters relevant to society. It is concluded that although the country is experiencing a complex crossroads in terms of the transition towards risk policies, a challenging effort would be to combine regulatory efficiency both in traditional risk problems and in the new criteria of the global development agenda.

Self-training for handwritten word recognition and retrieval

Handwritten text recognition and Word Retrieval, also known as Word Spotting, are traditional problems in the document analysis community. While the use of increasingly large neural network architectures has led to a steady improvement of performances it comes with the drawback of requiring manually annotated training data. This poses a tremendous problem considering their application to new document collections. To overcome this drawback, we propose a self-training approach that allows to train state-of-the-art models for HTR and word spotting. Self-training is a common technique in semi-supervised learning and usually relies on a small labeled dataset and training on pseudo-labels generated by an initial model. In this work, we show that it is feasible to train models on synthetic data that are sufficiently performant to serve as initial models for self-training. Therefore, the proposed training method does not rely on any manually annotated samples. We further investigate visual and language properties of the synthetic datasets. In order to improve performance and robustness of the self-training approach, we propose different confidence measures for both models that allow to identify and remove erroneous pseudo-labels. The presented training approach clearly outperforms other learning-free methods or adaptation strategies under the absence of manually annotated data.

Optimizing Route and Speed under the Sulfur Emission Control Areas for a Cruise Liner: A New Strategy Considering Route Competitiveness and Low Carbon

In order to reduce pollution caused by ship emissions, the International Maritime Organization (IMO) implemented sulfur emission control areas (SECAs). In comparison to ordinary vessels, cruise ships with dual attributes of transportation and tourism generate a greater amount of marine pollution, which poses a significant threat to the marine environment in both berthing ports and the sailing area. In light of the fierce competition of the cruise tourism market, cruise lines are looking for strategies, such as designing more attractive cruise routes, to maintain their core competencies under the emission control policy. In order to achieve this goal, this paper presents a mixed-integer non-linear programming (MINP) model with two objectives and is derived from the traditional route optimization problem. The primary objective is to optimize the route and speed of a cruise liner, while simultaneously enhancing route competitiveness and minimizing carbon emissions both within and outside the SECAs. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was used to reach the objective, and simulations were carried out to verify the effectiveness of the model and method. The results show that speed and sailing route optimization can affect carbon emissions. This paper has a certain application value and guiding significance for cruise line decision makers that will be beneficial for the environment.

Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning techniques have been extensively employed in the detection of colorectal polyps. However, these algorithms have not yet addressed the issue of detection in low-resolution images. In this study, we propose a novel YOLO-SRPD model by integrating SRGAN and YOLO to address the issue of low-resolution colonoscopy images. Firstly, the SRGAN with integrated ACmix is used to convert low-resolution images to high-resolution images. The generated high-resolution images are then used as the training set for polyp detection. Then, the C3_Res2Net is integrated into the YOLOv5 backbone to enhance multiscale feature extraction. Finally, CBAM modules are added before the prediction head to enhance attention to polyp information. The experimental results indicate that YOLO-SRPD achieves a mean average precision (mAP) of 94.2% and a precision of 95.2%. Compared to the original model (YOLOv5), the average accuracy increased by 1.8% and the recall rate increased by 5.6%. These experimental results confirm that YOLO-SRPD can address the low-resolution problem during colorectal polyp detection and exhibit exceptional robustness.

An efficient scheme for nonlinear shock wave model in a fractal domain under Caputo fractional operator.

This paper introduces a refined approach for obtaining the analytical solution of the nonlinear shock wave model incorporating fractal derivatives. The Fractal Yang Variational Iteration Strategy (FYVIS) is utilized to obtain the approximate solution of a fractal model in the form of a series under Caputo fractional operator. The suggested method is the composition of the fractal Yang transform and the variational iteration approach. By using the two-scale fractal theory, we transform the fractal model into its traditional problem and then apply the yang transform to generate a recurrence relation. The variational iteration approach is now suitable to handle this recurrence relation without imposing any hypotheses or restrictions on variables. The derived results by the proposed scheme are shown in terms of series solution. Numerical calculations verify the accuracy and consistency of the suggested approach, demonstrating its excellent performance. The dynamic behavior of fractal components is explored by evaluating absolute errors and presenting two-dimensional diagrams across the fractal domain. This investigation underscores that the suggested technique offers an efficient and user-friendly solution for solving the nonlinear shock wave model involving fractal derivatives.

Digital Twin Smart City: Integrating IFC and CityGML with Semantic Graph for Advanced 3D City Model Visualization

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard’s levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study’s results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning.

DeGAN - Decomposition-based unified anomaly detection in static networks

Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different forms: anomalous nodes, anomalous edges, and anomalous sub-graphs. It is crucial to detect all these anomaly types within a single framework to provide a unified solution to the graph anomaly detection task. The main objective of this study is to propose a model that is capable of detecting all static graph anomalies in a single architecture across various domains. In this paper, we introduce DeGAN (Decomposition-based unified Graph ANomaly detection), a novel framework for unified graph anomaly detection in static networks. DeGAN combines two deep learning concepts with graph decomposition to identify anomalous graph objects: a graph neural network and an adversarial autoencoder. DeGAN is featured with its capability to detect anomalies in a single process, and adopting graph decomposition has improved performance compared to the traditional adversarial learning approach. DeGAN is evaluated with six real-world datasets to demonstrate that our framework can work in multiple domains. Experimental results demonstrate that DeGAN is capable of detecting anomalous nodes, edges, and sub-graphs within a single model. Additionally, the effectiveness of the sub-components of DeGAN has been demonstrated through experimentation. Even though DeGAN is proposed for plain graphs, it can be extended to attributed and dynamic graphs.

The Relationship Between the Development of P2P Platforms and Economic Stability

The rapid development of China's peer-to-peer (P2P) lending platforms has not only injected new vitality into the financial market but also had a profound impact on the traditional banking system. With its efficient information matching and capital circulation mechanism, the P2P platform has solved the traditional loan problem and promoted the financing convenience of small and medium-sized enterprises. However, its rapid development has also brought a series of risks and challenges, such as economic stability problems, potential risk spread, etc. Compared with other countries, especially the United States, we find that the development of P2P platforms in different countries presents different characteristics. To meet these challenges, we need to strengthen supervision, regulate the operation behavior of P2P platforms, ensure the safety of funds, and prevent risks. At the same time, we should also pay attention to its interactive relationship with the traditional banking system, to achieve complementary development, and jointly promote the prosperity and stability of the financial market. To sum up, the development of the P2P platform has brought new opportunities and challenges to China's financial market. We need to deal with it with a prudent attitude and innovative thinking to ensure its healthy and orderly development.

Uncertainties associated with integral-based solutions to geodetic boundary-value problems

Physical geodesy applies potential theory to study the Earth’s gravitational field in space outside and up to a few km inside the Earth’s mass. Among various tools offered by this theory, boundary-value problems are particularly popular for the transformation or continuation of gravitational field parameters across space. Traditional problems, formulated and solved as early as in the nineteenth century, have been gradually supplemented with new problems, as new observational methods and data are available. In most cases, the emphasis is on formulating a functional relationship involving two functions in 3-D space; the values of one function are searched but unobservable; the values of the other function are observable but with errors. Such mathematical models (observation equations) are referred to as deterministic. Since observed data burdened with observational errors are used for their solutions, the relevant stochastic models must be formulated to provide uncertainties of the estimated parameters against which their quality can be evaluated. This article discusses the boundary-value problems of potential theory formulated for gravitational data currently or in the foreseeable future used by physical geodesy. Their solutions in the form of integral formulas and integral equations are reviewed, practical estimators applicable to numerical solutions of the deterministic models are formulated, and their related stochastic models are introduced. Deterministic and stochastic models represent a complete solution to problems in physical geodesy providing estimates of unknown parameters and their error variances (mean squared errors). On the other hand, analyses of error covariances can reveal problems related to the observed data and/or the design of the mathematical models. Numerical experiments demonstrate the applicability of stochastic models in practice.

A Computer Vision Engineering Management System for Automated Defect Detection in Electronic Components Manufacturing

Traditional digital image processing techniques face problems such as complex feature extraction and weak robustness when dealing with surface defects of multiple categories of electronic components. Deep learning is widely used in industrial defect detection. However, the performance of electronic component defect detection at the pixel segmentation level needs to be improved. For pixel-level defect detection, this paper constructs a defect detection model (ECSDDNet) for electronic component surface defects in computer vision engineering management system. To improve the segmentation accuracy and detection effect, three stages of experiments are conducted to address mis-segmentation problems and the shortcomings of the Unet network structure. Firstly, a classification network that can perform weight transfer is used to replace the encoding structure in the Unet network. Secondly, a simplified version of the feature fusion is proposed and added to the skip connection of the Unet network. Finally, label smoothing is used to optimize the loss and improve the generalization of the network. After the optimization experiment, some noisy contours and small defect contours that are mis-segmented are removed. Experimental results show that ECSDDNet has good segmentation effects on electronic component surface defects and can meet the segmentation and detection needs of electronic component surface defects.

Relationship between the Significant Solutions of Static Traffic Assignment Problems for Mixed Traffic Flow of Connected and Automated Vehicles and Human-Driven Vehicles

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology’s efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads.