Industrial Objects Research Articles

Optimization of vacuum belt drying process conditions for Citri Reticulatae Pericarpium using genetic algorithm and particle swarm optimization

Vacuum Belt Drying (VBD) is commonly applied in the pharmaceutical industry and its optimization is often required for different product processing. This study focuses on the optimization of process parameters for vacuum belt drying of Citri Reticulatae Pericarpium using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). VBD equipment control parameters are numerous, and lack of mature commissioning methods. Optimizing VBD process parameters can improve production efficiency and reduce production costs. The objective function was based on a numerical model of VBD. This model accurately predicts the moisture content variation during the drying process of Citri Reticulatae Pericarpium with a mean squared error (MSE) value of 0.0353. After that, the model introduces the formula for the income-expenditure ratio as the industrial optimization objective. The multi-objective problem of high yield and low energy consumption was transformed into a single objective by minimizing the income-expenditure ratio. After process optimization, the income-expenditure ratio was reduced by 3.80 and 4.02% compared to the original process conditions. A comparison between GA and PSO optimization results revealed that PSO performed better in optimizing the VBD process conditions, demonstrating superior convergence speed and optimization outcomes. In conclusion, this research emphasizes the effectiveness of utilizing GA and PSO algorithms to optimize process parameters for VBD. The algorithms provide optimized process conditions for VBD, replacing traditional empirical adjustment methods. This study contributes to the advancement of VBD technology by providing a systematic approach for optimizing process parameters.

PA-Pose: Partial point cloud fusion based on reliable alignment for 6D pose tracking

Learning-based 6-DOF (6D) pose tracking, serving as a basis for most real-time applications such as augmented reality and robot manipulation, receives attention transiting from 2D to 3D vision, with the popularity of depth sensors. However, the irregular nature of 3D point clouds challenges this task, especially since the lack of explicit alignments hinders the interaction and fusion between the observed point clouds. Therefore, this paper proposes a novel approach named PA-Pose to achieve 6D pose tracking in point clouds. It takes the forward-predicted dense correspondences within an overlap as reliable alignments, to guide the feature fusion of the partial-to-partial point clouds. Then, the relative transformation pose of adjacent observations is continuously regressed from the point-wisely fused features by confidence scoring, avoiding non-differentiable pose fitting. In addition, a shifted point convolution (SPConv) operation is introduced in the fusion process, to further promote the local context interaction of the observed point cloud pair in the expanded alignment field. Extensive experiments on two benchmark datasets (YCB-Video and YCBInEOAT) demonstrate that our method achieves state-of-the-art performance. Even though only 3D point clouds are taken as input, our PA-Pose is still competitive with those methods fully utilizing RGB-D information in the single view. Finally, experiments in the real scene for tracking industrial objects also validates the effectiveness of the proposed method.

Keypoint regression strategy and angle loss based YOLO for object detection

The YOLOv4 approach has gained significant popularity in industrial object detection due to its impressive real-time processing speed and relatively favorable accuracy. However, it has been observed that YOLOv4 faces challenges in accurately detecting small objects. Its bounding box regression strategy is rigid and fails to effectively leverage the asymmetric characteristics of objects, limiting its ability to enhance object detection accuracy. This paper proposes an enhanced version of YOLOv4 called KR–AL–YOLO (keypoint regression strategy and angle loss based YOLOv4). The KR–AL–YOLO approach introduces two customized modules: an keypoint regression strategy and an angle-loss function. These modules contribute to improving the algorithm’s detection accuracy by enabling more precise localization of objects. Additionally, KR–AL–YOLO adopts an improved feature fusion technique, which facilitates enhanced information flow within the network, thereby further enhancing accuracy performance. Experimental evaluations conducted on the COCO2017 dataset demonstrate the effectiveness of the proposed method. KR–AL–YOLO achieves an average precision of 45.6%, surpassing both YOLOv4 and certain previously developed one-stage detectors. The utilization of keypoint regression strategy and the incorporation of robust feature fusion contribute to superior object detection accuracy in KR–AL–YOLO compared to YOLOv4.

Information Modeling of Energy Saving Processes in the Field of Design, Construction and Operation

Statement of the problem. Improving energy efficiency at various stages of the life cycle of objects in the construction industry, which is an absolute imperative of the current stage of development of society, can be achieved by integrating information modeling of the life cycle of a capital construction object with energy saving technologies. However, in domestic and foreign practice, the potential of such an integration is realized only to a small extent. In this regard, this work is devoted to the formulation of methods for overcoming this situation. Results. It is shown that the addition of the base vectors of hierarchical information models with data on the thermophysical properties of isotropic materials and orthotropic elements allows solving the research problem. Also, the formats of basic structures and clusters of higher ranks have been developed, which completely determine the information model of the energy efficiency of the life cycle of an object. Methods for optimizing the technology of information transfer between heterogeneous participants in the implementation of the life cycle are formulated. For standard construction objects, methods for simplifying the energy saving information model are formulated, which allow expanding the area of practical application of the developed models. Conclusions. The integration of technologies for information modeling of the life cycle of an object and energy saving makes it possible to optimize managerial and technical decisions to increase the energy efficiency / cost ratio both at the design stage and at the stages of construction and operation. The vector hierarchical model of energy efficiency of the project life cycle allows optimizing the updating and verification of the information model, and the mechanisms for transferring information between project participants.

SECONDARY OAK FORESTS OF THE Primorye Territory AS A RESERVE OF RARE AND ENDANGERED VASCULAR PLANTS SPECIES

More than 60 species of rare and endangered vascular plant species are found in forests with oak. About 20 protected species grow in secondary oak forests adjacent to cities or settlements. The article provides information about the presence of rare plants in particular areas. Rare species are quite resistant to such anthropogenic influences as early spring fires, which oak forests experience almost annually. The greatest danger to the conservation of plant biodiversity is the cutting down of secondary oak forests for the construction of linear, industrial and civil objects.

Tribo-Electric Analysis of Multi-Cycle Wear in Asymmetric Laser-Structured Cu-Sn Connectors

One of the key objectives of the connector industry for the past years has been to reduce the insertion friction forces of detachable electrical connections, where the majority of approaches combine a particular connector design with the usage of lubricants. The necessity for a solution that uses less contact force to maintain a lasting electrically conductive connection without sacrificing the mechanical robustness of the connection during use is a significant challenge in this situation. The approach in this work is to create surfaces supporting asymmetric friction, meaning lower insertion than removal forces. By Direct Laser Interference Patterning (DLIP), asymmetric saw-tooth structures with different structural inclinations and periodicities were generated on Sn-coated Cu plates. It is determined that topographical interlocking is the primary operating mechanism providing the required anisotropy. Multiple insertion/removal cycles were simulated on the tailored surfaces to tribologically characterize them and to determine the evolution of the electrical contact resistance at the end of each insertion motion. The vast majority of the generated structures showed an improvement compared to the reference state, with lower insertion and higher removal forces while the electrical behavior is not significantly impacted.

Synthetic Data Generation Based on RDB-CycleGAN for Industrial Object Detection

Deep learning-based methods have demonstrated remarkable success in object detection tasks when abundant training data are available. However, in the industrial domain, acquiring a sufficient amount of training data has been a challenge. Currently, many synthetic datasets are created using 3D modeling software, which can simulate real-world scenarios and objects but often cannot achieve complete accuracy and realism. In this paper, we propose a synthetic data generation framework for industrial object detection tasks based on image-to-image translation. To address the issue of low image quality that can arise during the image translation process, we have replaced the original feature extraction module with the Residual Dense Block (RDB) module. We employ the RDB-CycleGAN network to transform CAD models into realistic images. Additionally, we have introduced the SSIM loss function to strengthen the network constraints of the generator and conducted a quantitative analysis of the improved RDB-CycleGAN-generated synthetic data. To evaluate the effectiveness of our proposed method, the synthetic data we generate effectively enhance the performance of object detection algorithms on real images. Compared to using CAD models directly, synthetic data adapt better to real-world scenarios and improve the model’s generalization ability.

What Is the Impact of the Renewable Energy Power Absorption Guarantee Mechanism on China’s Green Electricity Market?

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China’s green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms.

Perspectives of Contributing Factors and Prevention Measures of Struck-by Accidents in from Experienced Safety Personnel

Malaysia’s construction industry has been long characterized as risky, as evidencedby its poor health and safety performance. The statistics on fatal accidents recorded by the Department of Safety and Health Malaysia (DOSH) present that struck by accident in the Malaysian construction industry have the highest number of deaths and were one of the critical contributor. Therefore, this study was done to find the main contributing factor to the struck by accidents and its preventive measures. The questionnaire was distributed to the respondents, who were experienced safety personnel in construction industry with at least 10 years’ experience in handling safety and health at the workplace. From the questionnaire survey, the most significant contributing factor to struck-by accidents is carelessbehaviour, and the main perceived prevention measure for struck-by accident is job hazard analysis or job safety analysis briefing to workers before starting job. The outcome of the study could provide awareness on struck-by accidents and reduce the risk of struck-by accident by objects in the construction industry in Malaysia.

Self-training based augmented reality for robust 3D object registration and task assistance

Typical augmented reality (AR) technologies superimpose virtual information onto the real environment for task guidance and assistance by tracking fiducial markers. However, these have narrow recognition ranges due to the markers, and it is difficult to track markers when occlusion occurs. Therefore, it is challenging to seamlessly register real objects in AR scenes captured by the camera without requiring fiducial markers. Deep learning–based approaches to 3D object registration and task assistance have recently indicated promising directions for superimposing 3D virtual objects onto corresponding real objects or their surroundings seamlessly. However, since supervised learning-based object registration requires many pose annotations on images for the locations and orientations of real objects, this process is a time-consuming and labor-intensive task. Thus, this paper proposes a new self-training based augmented reality method for seamless real object registration and task assistance without time-consuming pose annotations. As the industrial components or objects have their 3D geometric models, the proposed approach utilizes them as their synthetic data. The automatically generated synthetic dataset is used for training the deep learning model, which outputs a colored coordinate map of objects to be detected. 2D–3D key point matching between the 2D colored coordinate map and 3D colored coordinate model is then performed for 3D object registration in an AR environment. In particular, self-training is performed for fine-tuning the deep learning model by generating pseudo-labels with the highest probability for mask intersection over union (IoU) in the unlabeled real object dataset. We compared the proposed approach with previous studies to confirm its effectiveness and advantages. Also, we performed ablation studies by training a custom dataset based on various industrial 3D virtual models. Finally, we present AR-based task guidance and assistance, such as industrial robot manipulation.

Study on the aesthetic evaluation method of applying symmetry analysis in industrial product styling design

Abstract The evaluation of product styling design is transformed into a complex nonlinear solution problem by symmetry analysis in this paper. The equilibrium matrix decomposition evaluation system calculates the stored matrix singular values in product styling design and proves that symmetry can be obtained from the stress matrix row vector. The regression algorithm is used to process multiple industrial product design objectives, and each design element in product styling is coded to obtain the fitness evaluation of individual industrial products. It is found that the satisfaction score in product styling design is 97.4, the weight index of logo novelty element is up to 7, and the regression coefficient of plane shape is 0.269, to improve the scientific and feasibility of aesthetic evaluation of industrial product styling design.

Modified Polyethylene Foams for Insulation Systems.

Effective insulation of buildings and other industrial objects requires the use of materials and system solutions that ensure maximum uniformity and density of insulation shells. The study focuses on the development of insulation systems based on expanded polyethylene and, in particular, on the development of modified polyethylene with reduced flammability containing a flame-retardant modified montmorillonite clay, which does not hinder gas formation, and silicate nanofillers in layered construction. Active experiments based on mathematical design methods allowed us to establish an analytical relationship between flame-retardant and modifier consumption and extruder pressure and response functions: average density of polyethylene foam and flammability criterion. The flammability criterion was taken as the oxygen index of the modified polyethylene foam. A foaming agent masterbatch was used as the flame retardant. Analytical optimization of mathematical models obtained as a result of active experiments allowed us to determine the optimal flame-retardant consumption, which was 3.7-3.8% of the polymer mass. Optimised systems for average density and oxygen index of flammability of modified polyethylene were obtained. A nomogram for predicting the material properties and selecting the composition, and an algorithm for a computer program for evaluating the properties of modified polyethylene foam as a function of the values of various factors, were developed. Taking into account the possible expansion of the scope of application of rolled polyethylene foam and seamless insulation shells based on it, possible solutions for insulation systems were studied using the program THERM, and a combined insulation system was adopted.

GROWING TREE METHOD FOR OPTIMISATION OF MULTIFACTORIAL EXPERIMENTS

Context. The task of planning multifactorial experiments is important in science and industrial production. In the context of competition, rising costs, and increasing efficiency, it is necessary to optimize plans for multifactorial experiments in terms of cost and time. To solve this problem, there are a number of approaches and methods, the choice of which for a competitive technical task is an important and difficult task. In this regard, there is a need to develop new methods for optimizing the cost (time) of multifactorial experiment plans, compare them with existing methods, and give recommendations for practical application in the study of real objects.
 Objective. The purpose of the study is to develop and test the method of growing trees, to evaluate its effectiveness in comparison with other methods. The following tasks has been solved to achieve this goal: the proposed method of growing trees has been implemented in the form of software; the method has been used to optimize plans for multifactorial experiments in the study of real objects; its effectiveness has been evaluated in comparison with other methods; recommendations for its use were given.
 Method. The proposed method of growing trees is based on the application of graph theory. The advantage of the method is the reduction of time for solving optimization problems related to the construction of optimal plans for multifactorial experiments in terms of cost (time) expenses. Another characteristic feature is the high accuracy of solving optimization problems.
 Results. The results of experiments and comparisons with other optimization methods confirm the efficiency and effectiveness of the proposed method and allow us to recommend it for the study of objects with the number of significant factors k ≤ 7. It is promising to further expand the range of scientific and industrial objects for their study using this method.
 Conclusions. A growing tree method has been developed for the optimization of multifactorial experimental plans in terms of cost and time expenditures, along with software that implements it using the Angular framework and the TypeScript programming language.
 The effectiveness of the growing tree method is shown in comparison with the following methods: complete and limited enumeration, monkey search, modified Gray code application, and bacterial optimization. The growing tree method is faster than complete enumeration and can be applied to optimize multifactorial experimental plans in terms of cost (time) expenses for objects with a number of factors k ≤ 7. In solving optimization problems, the method of growing trees gives better results compared to monkey search, limited enumeration and bacterial optimization.

Computational Metrology for Measuring Industrial Component Dimensions

To target the problem of dimension measurement of objects in industry, a new computer vision method is proposed based upon Harris-corner detection. The proposed research delivers an alternative to the requirement of various precise measurement devices and skilled labour. In order to measure the various dimensions of an object, the proposed algorithm separates the corner points from the background based on variations in pixel intensity. An algorithm has been proposed to analyze captured object images and perform measurements and inspection processes. The aim of this paper is to utilize computer vision detection algorithms to control the quality of manufactured parts by sorting them on size tolerance. The length of various objects such as screws, bolts, and a rectangular iron piece was determined from the images captured using smartphone camera (Samsung Galaxy F62). The evidence for a total of eight different measurements is presented, and the accuracy of the method is proved up to 99 percent against the dimensions measured using the Vernier calliper.

Effect of Changes in Planning on Project Objectives in Construction Industry of Pakistan

Effect of Changes in Planning on Project Objectives in Construction Industry of Pakistan

The Impact of Innovation Engagement, Functional Engagement, Emotional Engagement, and Communal Engagement on User Loyalty in Social Media Banking Industry

The purpose of this study was to investigate the impact of user loyalty in social media banking on innovation engagement, functional engagement, emotional engagement, and communal engagement. With a sample of 223 banking customers who actively use social media in Indonesia, this study used survey and purposive sampling methods. Data was collected using a Google Form online questionnaire. SEM-PLS is used to process the collected data. This study tested the hypothesis that user loyalty is affected by innovation engagement, functional engagement, and emotional engagement. User loyalty is unaffected by communal engagement. As far as is known, this study is still not much research with social media objects in the banking industry compared to previous studies. The findings of this study are expected to assist Indonesian banks in determining the best strategy for increasing social media engagement in order to increase customer loyalty.

The primary assessment of the industrial strategic priorities effectiveness

Industrial development is one of the most important areas contributing to the achievement of the strategic goal of ensuring the socio-economic development of any state or region. The success of the implementation of the designated goal is seen through a comprehensive process of strategizing industrial objects for a long period of time, namely through the effective development and implementation of signifi cant strategic priorities, which is especially relevant and important in post-normal conditions.Meanwhile, the complex work of strategy developers at the fi rst stage determines a long list with concepts of industrial strategic priorities, the implementation of each of them requires a certain provision, while the strategizing object has a limited supply of resources and time. Thus, it is necessary to select from this long list those strategic priorities that will give the highest effi ciency with the least expenditure of resources and time.In the article, the author proposes an expanded methodology for the primary assessment of the industrial strategic priorities effectiveness, which includes the process of parameterization of indicators for the estimated effects of industrial priorities, modeling and evaluation of the studied effects, the process of ranking industrial strategic priorities.The proposed methodology for evaluating the primary assessment of the industrial strategic priorities effectiveness allows ranking them at the fi rst stages of strategizing and forming a short list, on the basis of which the strategy development team will continue to work on detailing, formalizing and compositioning all developed elements at the conceptual level into a single fi nal document – an industrial strategy. In addition, the author describes the main features, principles and assumptions of the extended methodology.

SISG-Net: Simultaneous instance segmentation and grasp detection for robot grasp in clutter

Robots have always found it challenging to grasp in cluttered scenes because of the complex background information and changing operating environment. Therefore, in order to enable robots to perform multi-object grasping tasks in a wider range of application scenarios, such as object sorting on industrial production lines and object manipulation by home service robots, we innovatively integrated segmentation and grasp detection into the same framework, and designed a simultaneous instance segmentation and grasp detection network (SISG-Net). By using the network, robots can better interact with complex environments and perform grasp tasks. In order to solve the problem of insufficient fusion of the existing RGBD fusion strategy in the robot field, we propose a lightweight RGB-D fusion module called SMCF to make modal fusion more efficient. In order to solve the problem of inaccurate perception of small objects in different scenes, we propose the FFASP module. Finally, we use the AFF module to adaptively fuse multi-scale features. Segmentation can remove noise information from the background, enabling the robot to grasp in different backgrounds with robustness. Using the segmentation result, we refine grasp detection and find the best grasp pose for robot grasping in complex scenes. Our grasp detection model performs similarly to state-of-the-art grasp detection algorithms on the Cornell Dataset. Our model achieves state-of-the-art performance on the OCID Dataset. We show that the method is stable and robust in real-world grasping experiments. The code and video of our experiment used in this paper can be found at: https://github.com/meiguiz/SISG-Net.

AMSA-CAFF Net: Counting and high-quality density map estimation from X-ray images of electronic components

Accurately estimating object numbers plays a crucial role in industrial applications. However, object counting in computer vision poses a formidable challenge, particularly when dealing with images containing tiny, similar, and stacked objects. Therefore, this paper proposes an encoder–decoder-based convolutional neural network named AMSA-CAFF Net to accurately count and regress high-quality density maps from X-ray images of highly dense microscopic components. AMSA-CAFF Net has three major components: an adaptive multi-scale feature aggregation (AMSA) module for encoding adaptive multi-scale features in the encoder, a channel-wise adaptive feature fusion (CAFF) module to ensure smooth feature fusion, and a continuous feature enhancement mechanism to promote information integration in the decoder. In addition, there is still a lack of datasets dedicated to industrial object counting tasks. To address the limitations associated with the existing datasets, we construct a novel and large-scale object counting dataset named X-ray-based industrial electronic component counting dataset (XRAY-IECCD), consisting of 1,460 X-ray images of 10 types of components, resulting in a total of 2,915,126 objects annotated with points. To the best of our knowledge, the XRAY-IECCD is the first dataset of tiny, dense, multi-class object counts with a substantial number of annotations. We evaluate the performance of AMSA-CAFF Net on three datasets: the XRAY-IECCD, crowd counting benchmark ShanghaiTech dataset, and object counting benchmark CARPK dataset. The proposed framework outperforms the state-of-the-art approaches over the proposed XRAY-IECCD and CARPK datasets, highlighting its effectiveness. Furthermore, we achieve competitive results on the ShanghaiTech dataset, which demonstrates the generalization capability of our method.

Using spectrum allocations to address indigenous rights claims: The case of New Zealand

In February 2022, the New Zealand Government announced that a yet-to-be-formed “Māori Spectrum Entity” would “receive an ongoing allocation of 20 percent of future national commercial spectrum allocations, at no cost.” This is in addition to the 25 percent of spectrum designated for 5G technology (mid-band, 3.4–3.8 GHz) under the Māori Spectrum Working Group agreement. The novel arrangement, which creates a perpetual obligation rather than simply a one-off transfer, appears to settle a long-standing dispute between the Māori indigenous people of New Zealand and the Government regarding a claim under an 1840 treaty that Māori were entitled to a share of spectrum due to their historic property rights and the Government's obligation to ensure that their language could flourish. Further support apparently derives from New Zealand's obligations as a signatory to the United Nations Declaration on the Rights of Indigenous Peoples.The decision is notable for the lack of analysis of the expected effects on long-standing sector competition and efficiency objectives in the New Zealand telecommunications industry. Importantly, it introduces a tension into New Zealand spectrum policy between the (historic) allocation of spectrum rights to achieve the most economically efficient future outcome for the benefit of all New Zealanders (including all Māori), and the (post-memorandum) allocation where a preferential set-aside has been created to address a narrow distributional objective in spectrum “ownership” and governance involving only a subset of the population identifying as Māori.While raising the latter question, this paper addresses the competition and economic efficiency issues absent so far in official consideration of the arrangement. Using comparative economic and policy analysis, we find that while wealth and control is transferred from the Crown to the Māori entity, the Māori entity faces very different opportunities and incentives to deploy the spectrum in the most socially-useful manner compared to commercial operators. The “set-aside” creates both artificial scarcity for commercial spectrum and alters the strategic options for all parties due to the Māori rights being perpetual and not subject to the same terms and conditions as commercial spectrum. Additional burdens are imposed on New Zealand telecommunications service consumers not borne by those in jurisdictions without such policies, and relative to the counterfactual of full open auction of the relevant rights.We conclude that the agreement effectively subjugates sector competition and efficiency objectives to distributional concerns. Furthermore, granting specific policy development rights to the Māori entity suggests the agreement is not simply a vehicle for administering Māori spectrum but instead establishes a constitutional “co-governance” partnership between a state agency and the Māori entity governing future New Zealand spectrum policy. These fundamental changes to spectrum policy are unprecedented internationally.