Redundant Objects Research Articles

The expressive injustice of being rich

According to limitarianism, it is morally impermissible to be too rich. We consider three main challenges to limitarianism: the redundancy objection, the inconclusiveness objection, and the commitment objection. As a distributive principle, we find that limitarianism fails to overcome the three objections—even taking recent theoretical innovations into account. Instead, we suggest that the core commitment of limitarianism can be drawn from the excess intuition. It entails that at some point, people's claims to retain wealth become qualitatively different: they become preposterous from the point of view of interpersonal morality and justification. Extreme wealth, we argue, adds a distinctive expressive reason to worry about inequality and insufficiency, compounding these other distributive injustices. In retaining or wasting excess wealth while others have too little, the wealthy send a message of complete disregard for the interests of their co-citizens. They express that their disadvantaged compatriots have a diminished moral standing.

SceneDirector: Interactive Scene Synthesis by Simultaneously Editing Multiple Objects in Real-Time.

Intelligent tools for creating synthetic scenes have been developed significantly in recent years. Existing techniques on interactive scene synthesis only incorporate a single object at every interaction, i.e., crafting a scene through a sequence of single-object insertions with user preferences. These techniques suggest objects by considering existent objects in the scene instead of fully picturing the eventual result, which is inherently problematic since the sets of objects to be inserted are seldom fixed during interactive processes. In this article, we introduce SceneDirector, a novel interactive scene synthesis tool to help users quickly picture various potential synthesis results by simultaneously editing groups of objects. Specifically, groups of objects are rearranged in real-time with respect to a position of an object specified by a mouse cursor or gesture, i.e., a movement of a single object would trigger the rearrangement of the existing object group, the insertions of potentially appropriate objects, and the removal of redundant objects. To achieve this, we first propose an idea of coherent group set which expresses various concepts of layout strategies. Subsequently, we present layout attributes, where users can adjust how objects are arranged by tuning the weights of the attributes. Thus, our method gives users intuitive control of both how to arrange groups of objects and where to place them. Through extensive experiments and two applications, we demonstrate the potentiality of our framework and how it enables concurrently effective and efficient interactions of editing groups of objects.

YOLO-Chili: An Efficient Lightweight Network Model for Localization of Pepper Picking in Complex Environments

Currently, few deep models are applied to pepper-picking detection, and existing generalized neural networks face issues such as large model parameters, prolonged training times, and low accuracy. To address these challenges, this paper proposes the YOLO-chili target detection algorithm for chili pepper detection. Initially, the classical target detection algorithm YOLOv5 serves as the benchmark model. We introduce an adaptive spatial feature pyramid structure that combines the attention mechanism and the concept of multi-scale prediction to enhance the model’s detection capabilities for occluded and small target peppers. Subsequently, we incorporate a three-channel attention mechanism module to improve the algorithm’s long-distance recognition ability and reduce interference from redundant objects. Finally, we employ a quantized pruning method to reduce model parameters and achieve lightweight processing. Applying this method to our custom chili pepper dataset, we achieve an average precision (AP) value of 93.11% for chili pepper detection, with an accuracy rate of 93.51% and a recall rate of 92.55%. The experimental results demonstrate that YOLO-chili enables accurate and real-time pepper detection in complex orchard environments.

DiffRank: Enhancing efficiency in discontinuous frame rate analysis for urban surveillance systems

Urban public safety management relies heavily on video surveillance systems, which provide crucial visual data for resolving a wide range of incidents and controlling unlawful activities. Traditional methods for target detection predominantly employ a two-stage approach, focusing on precision in identifying objects such as pedestrians and vehicles. These objects, typically sparse in large-scale, lower-quality surveillance footage, induce considerable redundant computation during the initial processing stage. This redundancy constrains real-time detection capabilities and escalates processing costs. Furthermore, transmitting raw images and videos laden with superfluous information to centralized back-end systems significantly burdens network communications and fails to capitalize on the computational resources available at diverse surveillance nodes. This study introduces DiffRank, a novel preprocessing method for fixed-angle video imagery in urban surveillance. The method strategically generates candidate regions during preprocessing, thereby reducing redundant object detection and improving the efficiency of the detection algorithm. Drawing upon change detection principles, a background feature learning approach utilizing shallow features has been developed. This approach prioritizes learning the characteristics of fixed-area backgrounds over direct background identification. As a result, alterations in ROI are efficiently discerned using computationally efficient shallow features, markedly accelerating the generation of proposed Regions of Interest (ROIs) and diminishing the computational demands for subsequent object detection and classification. Comparative analysis on various public and private datasets illustrates that DiffRank, while maintaining high accuracy, substantially outperforms existing baselines in terms of speed, particularly with larger image sizes (e.g., an improvement exceeding 300 % at 1920×1080 resolution). Moreover, the method demonstrates enhanced robustness compared to baseline methods, efficiently disregarding static targets like mannequins in display windows. The advancements in candidate area preprocessing enable a balanced approach between detection accuracy and overall detection speed, making the algorithm highly applicable for real-time on-site analysis in edge computing scenarios and cloud-edge collaborative computing environments.

Automated code development based on genetic programming in graphical programming language: A pilot study.

Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field could, therefore, improve software engineers' common workflow and decrease the time to market. Even though many code-generating algorithms have been proposed in textual-based programming languages, to the best of the authors' knowledge, none of the studies deals with the implementation of such algorithms in graphical programming environments, especially LabVIEW. Due to this fact, the main goal of this study is to conduct a proof-of-concept for a requirement-based automated code-developing system within the graphical programming environment LabVIEW. The proposed framework was evaluated on four basic benchmark problems, encompassing a string model, a numeric model, a boolean model and a mixed-type problem model, which covers fundamental programming scenarios. In all tested cases, the algorithm demonstrated an ability to create satisfying functional and errorless solutions that met all user-defined requirements. Even though the generated programs were burdened with redundant objects and were much more complex compared to programmer-developed codes, this fact has no effect on the code's execution speed or accuracy. Based on the achieved results, we can conclude that this pilot study not only proved the feasibility and viability of the proposed concept, but also showed promising results in solving linear and binary programming tasks. Furthermore, the results revealed that with further research, this poorly explored field could become a powerful tool not only for application developers but also for non-programmers and low-skilled users.

Acquisition of representative objects and attribute reductions based on generalized decisions of dominance-based rough set approach

Dominance principles play a pivotal role in decision makings, in which the superior the object performs on criteria, the higher the object will be assigned by decision attributes. In practical issues, the result of decision makers is influenced significantly because of the existence on redundant objects and attributes. However, little work studies the inconsistency of dominance principles caused by redundant objects. The proposed generalized decision aims to intuitively reveal the consistency of dominance principles. Since then, considerable attention has been devoted to accelerating attribute reductions in dominance-based rough sets. But there has been limited discussion on the semantic explanation of generalized decisions. This paper addresses this gap by introducing a graded information granule that not only explains the consistency of dominance principles but also facilitates the partitioning of the universe. Based on this foundation, several theoretical properties are thoroughly investigated. Moreover, we acquire a representative object set by applying a threshold to filter out objects with an extreme imbalance of dominance principles, and a final compact data set is generated with the best threshold by a particular parameter selection mechanism. Additionally, we design an attribute reduction method based on graded information granules using the compact data set. Furthermore, we employ numerical data sets to illustrate the effectiveness and efficiency of our method by running time and importance of reducts performed from the perspective of dominance-based rough sets and machine learning. Finally, the practicability of compact data sets is explained from the viewpoint of decision rules in dominance-based rough set approach.

Rotating Object Detection for Cranes in Transmission Line Scenarios

Cranes are pivotal heavy equipment used in the construction of transmission line scenarios. Accurately identifying these cranes and monitoring their status is pressing. The rapid development of computer vision brings new ideas to solve these challenges. Since cranes have a high aspect ratio, conventional horizontal bounding boxes contain a large number of redundant objects, which deteriorates the accuracy of object detection. In this study, we use a rotating target detection paradigm to detect cranes. We propose the YOLOv8-Crane model, where YOLOv8 serves as a detection network for rotating targets, and we incorporate Transformers in the backbone to improve global context modeling. The Kullback–Leibler divergence (KLD) with excellent scale invariance is used as a loss function to measure the distance between predicted and true distribution. Finally, we validate the superiority of YOLOv8-Crane on 1405 real-scene data collected by ourselves. Our approach demonstrates a significant improvement in crane detection and offers a new solution for enhancing safety monitoring.

Fast attribute reduction via inconsistent equivalence classes for large-scale data

Feature selection, also known as attribute reduction, plays a crucial role in machine learning and data mining tasks. Rough set theory-based feature selection methods have gained popularity due to their ability to handle imprecise and inconsistent data, ease of implementation, and generation of highly interpretable results. However, these methods still suffer from high computational cost when dealing with large-scale datasets with high dimensions. To overcome this shortcoming, we propose a fast attribute reduction method based on inconsistent equivalence classes. The presented method can accelerate those attribute reduction algorithms whose importance measures used can be computed using only inconsistent equivalence classes. Our proposed method improves attribute reduction efficiency through three key aspects: 1) transforming the original dataset into an equivalently simplified version with fewer samples, 2) accelerating the computation of core attributes, and 3) expediting the forward selection process by removing redundant objects and attributes. Experimental results demonstrate the high computational efficiency of our proposed method.

Interactive fuzzy knowledge distance-guided attribute reduction with three-way accelerator

Attribute reduction with an accelerator is an efficient strategy to handle large-scale information systems, but it is rarely suitable for information systems with fuzzy decision (ISFD). Meanwhile, both three-way approximations (TA) and fuzzy knowledge distance (FKD) are two types of attractive uncertainty measures in ISFD. Therefore, it prompts us to deliberate on how to interact the merits of TA with FKD to develop an attribute reduction with a three-way accelerator for ISFD. Driven by this concern, the TA of fuzzy decision is used as prior knowledge to interact with FKD, then a model named ITA-FKD is constructed to explore the monotonicity of uncertainty in multi-granularity spaces and measure the significance of every attribute. A three-way accelerator is implemented to eradicate redundant objects and attributes with the aid of the ITA-FKD model. Subsequently, a filter-wrapper approximate attribute reduction with the unique accelerator is successfully devised. Ultimately, some comprehensive experiments with high-dimensional datasets containing biomedicine field exemplified the higher efficiency of the explored algorithm.

A novel low‐complexity model predictive control for Vienna rectifier

SummaryA Vienna rectifier is a kind of three‐phase converter with complex operation constraints. Traditional control methods suffer from poor dynamic responses and total harmonic distortion (THD), particularly when operating with adjustable wide‐range power. A novel low‐complexity model predictive control (LC‐MPC) algorithm is proposed based on the optimal switching vector sequence in this paper. First, a model predictive optimization control (MPOC) method is designed to search for the voltage vector sequence and its acting time. Second, the equivalent transformation and coordinate mapping of MPOC are efficiently achieved through the derived correlation factors and lookup table. Supported by the correlation factors, the redundant objective function calculation and repetitive online optimization are eliminated. Meanwhile, the simplified optimal over‐modulation strategy is implemented. Finally, the effectiveness and superiority of the algorithm are verified by comparative experiments. The results show that the proposed LC‐MPC is beneficial in terms of the computation time, dynamic response, over‐modulation, and harmonic content reduction.

Incorporating aggregation trees in two-archive algorithm 2 for solving high dimensional multi-objective electromagnetic optimization

The optimization designs of modern electromagnetic devices are often modeled as high dimensional multi-objective problems due to their high-quality demands in multiple functions and systemization. To identify and remove the redundant objectives, an interactive aggregation trees method embedded into the two-archive algorithm 2 is proposed to solve the high dimensional multi-objective optimizations. To balance the speed and the accuracy in finding the redundant objectives, conflict degree, conflict list and conflict flag are developed in an interactive aggregation trees frame. The two-archive algorithm 2 is modified to adjust the updated model obtained from the objective-reduction frame. Mathematical test functions and TEAM Workshop Problem 22 are used to test the performance of the proposed method. Numerical results demonstrate that the proposed method can remove the redundant objectives effectively and provide qualified solutions for the electromagnetic optimization designs.

An improved MOEA/D with dual objective-reduction method for antenna design

Due to the high requirements for performance of antennas in the modern communication system, more and more objectives need to be considered, which makes the antenna design unendurable time-consuming and hard to converge. To obtain the Pareto solutions for antenna design effectively and efficiently, a dual reduction method is incorporated in an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D). To obtain information about conflicting objectives on the Pareto front, the size of the MOEA/D neighbourhood is dynamically adjusted; to improve the speed and accuracy of finding redundant objectives, PCA and Pareto impact ratio are used as dual objective-reduction criteria. Numerical results in solving the mathematical benchmark problems, an antenna array problem and Yagi-uda optimal design demonstrate that the proposed method can reduce redundant objectives and increase the convergence speed.

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.

Wreckage Installation: Towards an Archaeology of Southern Sweden's Heterotopias

During a survey on the island of Öland in south-eastern Sweden, whose aim was to study the local waste-disposal practices, the authors recorded abandoned machinery and cars dating from the 1940s to today in locations close to residential areas and farms, and complemented the investigation by interviewing informants. This led them to conclude that dumping redundant objects in the surroundings of villages forms an entangled network with other behaviour, i.e. collecting things which had outlived their usefulness and embedding them in the landscape. The behaviour observed in Öland is compared with two other cases of collecting abandoned objects in Öland and southern Sweden. Using the location and chronology of the finds, the authors interpret the behaviour by borrowing the concept of heterotopia, as defined by Foucault.

Addressing climate uncertainty and incomplete information in transboundary river treaties: A scenario-neutral dimensionality reduction approach

• We enumerate 25,000 + solutions for the Ganges water sharing treaty, renewed in 2026. • Hierarchical clustering identifies redundant objectives and governing trade offs. • Pareto-optimization & scenario-neutral analysis finds climate-robust treaty solution. Climate change will alter the flow availability and expected water allocations in international river treaties, many of which were designed using historical flow records. Effective transboundary treaties should anticipate these concerns and seek to satisfy the priorities of all riparian countries while being robust to impending changes in climate. This task is complicated by the fact that specific objectives associated with each party’s priorities are not necessarily common knowledge (framing uncertainty), and the direction, amplitude and effect of long term changes in hydro-climatic drivers can be highly uncertain (climate uncertainty). We frame the design of a transboundary treaty as a multi-objective optimization problem. We use hierarchical clustering to address problem-framing uncertainty by identifying the subset of objectives associated with the governing trade-offs imposed by the bio-physical characteristics of the shared river system. We then carry out a scenario-neutral climate sensitivity analysis to identify climate-robust Pareto-optimal treaty solutions. We illustrate the approach for the Ganges water agreement, which is due to be renewed in 2026. Based on an enumerated population of 25,121 feasible treaty solutions, we identify governing objectives and 16 treaty solutions that are Pareto optimal under most considered combinations of changes in sea level and dry season flow regime. This work provides a path towards improving transboundary allocations for the Ganges water treaty and, more broadly, a template to support transboundary cooperation over shared international rivers.

Redundant object detection method for civil aircraft assembly based on machine vision and smart glasses

Slow detection of redundant objects and low accuracy in assembly lines, particularly in the setting of civil aircraft assembly, are tough and challenging problems. To address these issues, a redundant object detection method based on computer vision and augmented reality (AR) smart glasses is proposed in this paper. The method uses AR glasses as the image collection hardware and takes the live image collected by the camera as the input of the proposed deep learning machine vision model. The proposed model, the Feature Pyramid Networks-CenterNet, is inspired by CenterNet and combined with multi-scale feature fusion to solve the problem of low detection accuracy of small-scale redundant targets. The weight factor of the loss function was set according to the proportion of small targets in the dataset, which solves the problem of an unbalanced proportion of large and small targets in the training samples. The proposed network model was validated on the PASCAL Visual Object Classes public dataset and the self-built redundant object dataset. The results showed that the new method can detect seven redundant objects with a mean accuracy of 74.49% within the visible range of smart glasses within 200 ms. The research provides a new reference for the quality process management of civil aircraft assembly.

LiSurveying: A high-resolution TLS-LiDAR benchmark

Outdoor point-cloud object localization is an essential processing step for urban scene analysis and modeling in numerous applications, especially in land surveying and site analysis. Given the increasingly use of Terrestrial Laser Scanning (TLS) and LiDAR 3D data acquisition, numerous annotated point-cloud datasets are available and can be used to evaluate computer vision and machine learning based algorithms. Nevertheless, current point-cloud datasets mainly focus on object detection and classification in autonomous driving or urban planning type of applications, and share redundant object classes, e.g., trees, vehicles and pedestrians, which limit their usefulness for land surveying and site analysis. This paper introduces a novel 3D benchmark dataset LiSurveying, which is a large-scale point-cloud dataset with over a billion points and uncommon urban object categories in complex outdoor environments. Our dataset incorporates more urban object classes than existing datasets. Its instances have diverse point densities, shapes and dimensions, which also impose a challenge for point-cloud detection and classification algorithms. We conducted baselines experiments for point-cloud classification using machine learning classifiers and deep learning methods on different subsets of the LiSurveying dataset, and we are able to demonstrate that the various types of object classes, number of instances per class, distribution of object points, and variety of complex scenes, make this LiSurveying benchmark dataset suitable for evaluating 3D point-cloud classification, semantic segmentation, and object detection algorithms.

Multi-objective optimization of energy conservation and emission reduction in China's iron and steel industry based on dimensionality reduction

With increasingly stringent environmental restrictions, the iron and steel industry is faced with multiple targets such as energy conservation, emission reduction, and cost control, hence, making industrial-environmental management a multi-objective optimization (MOO) problem. Existing studies adopt MOO models to address this issue, however, their methods result in high-dimension problems that increase computational cost and the difficulty to formulate optimal management schemes. This study builds a constrained MOO model in China's iron and steel industry, and introduces a dimensionality reduction technique to identify redundant objectives which can be omitted without altering the Pareto dominance structure of the original high-dimension problem. NSGA-II is then applied on the resulting lower-dimension problem to optimize the application of five types of Energy Conservation and Emission Reduction (ECER) measures, consisting of 23-process equipment and 55-ECER technologies. Results show that: (1) The high-dimension problem can be reduced to a four objective optimization problem. The four key-objectives are cost, energy, CO2, and PM intensity control. Hence, ECER policies should focus on these four key-objectives since optimizing them will synergistically minimize redundant objectives like NOx and SO2 emissions; (2) For the resulting lower-dimension problem, solution quality increases as reflected by algorithm verification metrics (spacing metric and hypervolume indicator), while computational cost is reduced by 73% and the decision making process is simplified significantly; (3) By-production reutilization and cleaner production technologies are the most attractive measures, contributing about 60% of ECER potential and only 30% of total cost. Equipment upgrading also has good ECER effects, but requires strong economic incentives, while end-of-pipe and renewable energy technologies have the lowest ECER potential. In sum, this study proposed a methodology to reduce the complexity of ECER optimization under multiple objectives, and provided suggestions to enhance industrial-environmental management under increasing environmental restrictions.

Deviating from the ideal

AbstractIdeal theorists aim to describe the ideally just society. Problem solvers aim to identify concrete changes to actual societies that would make them more just. The relation between these two sorts of theorizing is highly contested. According to the benchmark view, ideal theory is prior to problem solving because a conception of the ideally just society serves as an indispensable benchmark for evaluating societies in terms of how far they deviate from it. In this paper, I clarify the benchmark view, argue that existing criticisms of it are unsuccessful, and develop a novel redundancy objection to the benchmark view and the claim of priority it allegedly entails. I then consider the extent to which ideal theory might facilitate problem solving without being prior to it and argue that it can only play a modest role in this regard. The upshot is that ideal theory is neither required for nor especially relevant to problem solving—but it is not completely irrelevant either. It facilitates problem solving to some limited degree, but no more, say, than theorizing about dystopia.

Research on Virtual Color Restoration of Complex Building System Based on Discrete Wavelet Transform

At present, image restoration has become a research hotspot in computer vision. The purpose of digital image restoration is to restore the lost information of the image or remove redundant objects without destroying the integrity and visual effects of the image. The operation of user interactive color migration is troublesome, resulting in low efficiency. And, when there are many kinds of colors, it is prone to errors. In response to these problems, this paper proposes automatic selection of sample color migration. Considering that the respective gray-scale histograms of the visual source image and the target image are approximately normal distributions, this paper takes the peak point as the mean value of the normal distribution to construct the objective function. We find all the required partitions according to the user’s needs and use the center points in these partitions as the initial clustering centers of the fuzzy C-means (FCM) algorithm to complete the automatic clustering of the two images. This paper selects representative pixels as sample blocks to realize automatic matching of sample blocks in the two images and complete the color migration of the entire image. We introduced the curvature into the energy functional of the p-harmonic model. According to whether there is noise in the image, a new wavelet domain image restoration model is proposed. According to the established model, the Euler–Lagrange equation is derived by the variational method, the corresponding diffusion equation is established, and the model is analyzed and numerically solved in detail to obtain the restored image. The results show that the combination of image sample texture synthesis and segmentation matching method used in this paper can effectively solve the problem of color unevenness. This not only saves the time for mural restoration but also improves the quality of murals, thereby achieving more realistic visual effects and connectivity.