Object Points Research Articles

Implementasi Linier Programming Modelling untuk Maksimasi Profit Penjualan Produk Seblak pada UD. XYZ di Aceh Barat

Seblak is a food that has seen an increase in demand in recent years, and is a promising culinary business that can be run with a small capital. UD. XYZ is a seblak business that started selling seblak in early 2022 in West Aceh and wants to maximize the profits from its seblak sales. One method that can do this is Linear Programming, which can determine decision-making that can group limited materials or resources to achieve an optimal or best solution. From UD. XYZ's weekly sales data, it can be transformed into a mathematical model that helps to find the maximum function of the profit from seblak sales. The linear programming calculation process in this study uses the Lingo 18.0 software to solve linear programming problems, which will be continued with the linear graph visualization of the objective function with the MATLAB software. The results of the study show that the maximum profit that will be obtained by UD. XYZ in one week is Rp. 420,000/week, this profit is already categorized as net profit that can be used for the development of UD. XYZ's seblak business. Of the three variables X1 (seblak noodles and crackers), X2 (seblak eggs), and X3 (seblak chicken/feet), variables X1 and X2 have the potential to increase maximum profit if increased. In this study, the objective point is also on the point axis (X1=0, X2=0, and X3 = 175).

Extraction of Urban Road Boundary Points from Mobile Laser Scanning Data Based on Cuboid Voxel

The accuracy of point cloud processing results is greatly dependent on the determination of the voxel size and shape during the point cloud voxelization process. Previous studies predominantly set voxel sizes based on point cloud density or the size of ground objects. Voxels are mostly considered square in shape by default. However, conventional square voxels are not applicable to all surfaces. This study proposes a method of using cuboid voxels to extract urban road boundary points using curb points as road boundary points. In comparison with conventional cubic voxels, cuboid voxels reduce the probability of mixed voxels at the road curb, highlight two geometric features of road curb voxels (i.e., normal vector and distribution dimension), and improve the accuracy of road curb point extraction. In this study, ground points were obtained using cloth simulation filtering. First, the cuboid-based voxelization of ground points was performed. Then, taking the voxel as a unit, two geometric features, namely, the normal vector of the voxel and the linear dimension of the point distribution in the voxel, were calculated. According to these geometric features, the voxels that met the conditions were regarded as candidate road curb voxels, and the points in them as candidate road curb points. Afterward, filtering was applied using the intensity value to eliminate the bottom points of fences, street trees, and other ground objects in the candidate road curb points. Finally, noise points were eliminated according to the clustering results of the density based spatial clustering of applications with noise (DBSCAN) algorithm. In this study, point cloud data obtained by the SSW vehicle-mounted mobile mapping system and three-point cloud datasets in the IQmulus & TerraMobilita competition dataset were used to experimentally extract road curbs. Results showed that this method could effectively extract road curb points as the precision of the four groups of data results was over 90% and the quality coefficient reached over 75%.

Multi-Criteria Decision-Making for Water Resources Planning Using the MOORA Method

Water Resources Planning. Systems with water resources have benefited people and their enterprises for a very long time. Many services are provided by these systems. But in many regions around the globe, people are Infrastructure that is inappropriate, insufficient, or in poor condition, excessive river flow withdrawals, pollution from agricultural and industrial operations, nutrient loadings that cause eutrophication, salinization from ground water flows, insect infestation of exotic animals and plants, excessive fish foraging, flood simple and habitat modification from development activities, but rather modifications in water quality and sediment flow regimes are some of the typical causes. Systems for managing water resources have long benefited Individuals and their economy. These systems provide a variety of services. But, they cannot meet all but the most basic needs for sanitization and drinking water in many regions of the world. Moreover, many of these water resource management regimes are unable to support and encourage resilient biodiverse ecosystems. Common causes include inappropriate, insufficient, and/or deteriorated infrastructure, excessive river flow withdrawals, pollution from agricultural and industrial operations, nutrient enrichment brought on by nutrient applied loads, salinization from groundwater recharge stream, infestations of exotic plants and animals, extreme fish collecting, flood plain and vegetation modification caused by construction events, and needs changing in water and sediment flow conditions. Decision Making (MODM) techniques are actually available, and this newsletter is prepared to introduce the new MODM methodology. Cross optimization ratio analysis in essence Unique AMS selection problems are resolved by the (MOORA) method in the context of real-time manufacturing. Using the method of the most objective reference point, house is one of MOORA's components. On the question of the owners' consumer sovereignty, several reservations may be made. Pre-SMART cost to complete (dollars), Post-SMART cost to complete (dollars), Dollars saved, Time saved (months). Ala Wai, Espanola Valley, LA River, Lower San Joaquin River, Missouri River degradation. “from the result it is seen that Ala Wai and is got the first rank whereas is the LA River got is having the lowest rank.” The value of the dataset for Water Resources Planning in MOORA method shows that it results in Ala Wai and top ranking”.

Path planning for unmanned automaton based on improved artificial potential field method

The artificial potential field method (APF), a highly effective navigation technique, is currently utilized extensively in this area due to the rapid development of unmanned automatons. Traditional APF, however, have several shortcomings, including the issue of unreachable object points and the propensity to sink into local minima that prohibit the automaton from moving on. In this paper, a two-part improved APF model is created to address these issues. First, by including additional constraints, the repulsive field model at the stumbling block is enhanced to address the issue that the object point is impassable when too close a distance between the two stumbling blocks. Secondly, a new potential field is introduced to help the automaton walk out of the local minima. Analogue simulation show that the methods mentioned above can solve these problems better and make the route planning of unmanned automatons come true.

Evaluation of the impact of the thermal state of a diesel engine on its efficiency

The paper presents the results of model and empirical research on the influence of the thermal state of a diesel engine (oil temperature) on its indicated (thermal) efficiency. The paper contains a test plan including a description of the test object, test equipment and measurement points on a real object. In the following part, the results of tests carried out on a real object (laboratory single-cylinder engine) and the results of model tests obtained on the original engine model are presented. The results are presented both in tabular and graphical form. The obtained test results allowed to determine the relative value of the influence of the engine's thermal state on its efficiency for various operating conditions (load and rotational speed).

Search for a possible quasi-periodic structure based on data of the SDSS DR12 LOWZ

ABSTRACT We carry out a statistical analysis of the spatial distribution of galaxies at cosmological redshifts 0.16 ≤ z ≤ 0.47 based on the SDSS DR12 LOWZ catalogue. Our aim is to search and study possible large-scale quasi-regular structures embedded in the cosmic web. We calculate projections of the Cartesian galaxy coordinates on different axes (directions), densely covering certain regions in the sky to look for special directions along which 1D distributions of the projections contain significant quasi-periodic components. These components appear as peaks in the power spectra and lie in a narrow range of wave numbers 0.05 < k < 0.07. Particular attention is paid to the evaluation of the significance of the peaks. It is found that the significance of the dominant peaks for some selected directions exceeds (4–5)σ. In order to reduce possible selection effects, we create a mock homogeneous catalogue of spatial distribution of galaxies by adding a random set of artificial objects (points) to the real galaxies under study. The power spectrum of this cumulative model data also demonstrates significant peak corresponding to approximately the same scale. As a result we assume the existence of an anisotropic cosmological quasi-periodic structure with characteristic scale (116 ± 10) h−1 Mpc.

Intelligent detection and behavior tracking under ammonia nitrogen stress

In this paper, a novel YOLO-based detection model with deformable convolution network (DCN-YOLOv5) is developed, which is concerned with the object and key points detection and behavior tracking problem for Oplegnathus punctatus in the ammonia nitrogen environment. The proposed model can adapt to the posture change of the object by deforming the receptive field, which solves the problem of false and missed detection caused by the movement and occlusion. Moreover, a new multi-object multi-category tracking algorithm (MOMC-Tracking) is proposed to track and plot the trajectory and calculate the key behavioral characteristics parameters. In addition, an executable software which integrates the proposed DCN-YOLOv5 model and the MOMC-Tracking algorithm is proposed. Extensive experiments show that compared with the typical YOLO series of algorithms, the proposed model in this paper performs the best with the highest accuracy and the fastest convergence speed, where the mAP@0.5 and mAP@0.5:0.95 of the proposed DCN-YOLOv5 model are 93.71% and 57.45%, which are respectively improved by 1.78% and 24.77% as compared with those obtained by the original YOLOv5 model.

Response of G-NUMEN LaBr3(Ce) Detectors to High Counting Rates

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (nuclear matrix element for neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory. This project aims to explore double-charge exchange (DCE) reactions in order to obtain crucial information about neutrinoless double beta decay (0νββ). The primary objective of the G-NUMEN array is to detect the gamma rays emitted from the de-excitation of the excited states that are populated via DCE reactions with a good energy resolution and detection efficiency, amidst a background composed of the transitions from competing reaction channels with far higher cross sections. To achieve this, G-NUMEN signals will be processed in coincidence with those generated by the detection of reaction ejectiles by the MAGNEX focal plane detector (FPD). Under the expected experimental conditions, G-NUMEN detectors will operate at high counting rates, of the order of hundreds of kHz per detector, while maintaining excellent energy and timing resolutions. The complete array will consist of over 100 LaBr3(Ce) scintillators. Initial tests were conducted on the first detectors of the array, allowing for the determination of their performance at high rates.

Öğretmenlerin Eğitim Yönetiminde Karşılaştıkları Ayrımcı Tutum ve Davranış Algılarının Anlaşılmasına Yönelik Veri Analizinin İncelenmesi

In this research, it is aimed to examine the data analysis aimed at understanding the perceptions of discriminatory attitudes and behaviors encountered by teachers in educational administration. This study was conducted in schools in Bağcılar district of Istanbul Province under the Ministry of National Education. The method of this qualitative study is case science (phenomenology) and the data were interpreted by descriptive analysis. The participants of the study consist of 16 teachers from different branches at kindergarten, elementary, middle and high school levels. In the findings obtained, it was concluded that discriminatory practices in schools undermine the teacher's sense of justice and merit, teachers take on a structure aimed at questioning their work ethics, and even experience an intense lack of motivation to want to leave school. According to the results obtained according to the gender distribution, it is observed that male teachers are discriminated more in schools where the managers are women. On the contrary, male administrators also show favor to female teachers in the opposite context. Especially in the education sector, it is very important for teachers to feel morale, motivation, work merit, a sense of equality in the sense of developing a sense of belonging to the institution. Based on these results, development can be achieved in areas such as sharing tasks fairly, having an objective point of view, and adopting the principle of equal distance in order to reduce discriminatory actions. Key Words: Teacher, Education Management, Discrimination, Data Analysis

The mediating role of obesity on the prospective association between urinary sucrose and diabetes incidence in a sub-cohort of the EPIC-Norfolk

Background/objectivesFindings from epidemiological studies showed controversial findings between dietary sugar intake and the development of diabetes. Most of these studies assessed dietary sugar intake by self-reports which might be prone to bias. Urinary sucrose, an objective biomarker of sucrose intake, might provide better insights into this association. Thus, the aim of this study was to investigate the associations between sucrose intake, measured via self-reports and urinary sucrose, with incident diabetes and to detect the impact of obesity on this association.Subjects/methodsData of a sub-group (n = 2996) from the prospective EPIC-Norfolk cohort were investigated. Sucrose intake was assessed by self-reports (validated food frequency questionnaire (FFQ) and 7-day diet diaries (7DD)) and as an objective urinary sucrose biomarker. Cox proportional hazard models were conducted to calculate hazard ratios (HRs) and 95% confidence intervals (CI) for the associations between urinary and dietary sucrose intake and incident diabetes. Mediation analysis was performed to investigate the mediated percentage of body mass index (BMI) and waist circumference (WC) on this association.ResultsThe mean age of the participants was 60.6 ± 9.5 years and 53% were women. After a mean follow-up of 11.2 ± 2.9 years, 97 participants developed diabetes. Findings suggested inverse associations regarding incident diabetes for self-reported sucrose intake per 50 g/d via 7DD [HR: 0.63 (95% CI: 0.43, 0.91)], and a tendency via FFQ [HR: 0.81 (95% CI: 0.46, 1.42)]. Urinary sucrose indicated a positive association with incident diabetes for each increase of 100 µM [HR: 1.14 (95% CI: 0.95, 1.36)]. The proportion mediated of BMI and WC for this association was 16 and 22%.ConclusionsThese findings indicate that sucrose measured as objective urinary biomarker points to a positive association with incident diabetes. BMI might partly mediate this association. However, to obtain more precise results, more studies are warranted that consider this objective biomarker.

Dynamic Rigid Bodies Mining and Motion Estimation Based on Monocular Camera.

Dynamic object perception is an important yet challenging direction in the field of robot navigation. Without any prior knowledge about motion and objects, a novel dynamic rigid bodies mining and motion estimation method based on monocular camera is proposed in this article. Different from the existing works based on sampling that associate feature points to motion hypotheses according to the reprojection errors, our work endeavors to find the intrinsic relevance among motion hypotheses. To represent this relevance, the concept of the probabilistic field on the Lie group Sim(3) manifold is introduced, which is established using random sampling. It provides a computable way for the regions on the manifold where rigid bodies possibly appear. The probability of a motion hypothesis falling on a region is expressed by its confidence. The regions with large confidences in the probabilistic field are selected as potential rigid bodies, whose corresponding feature points are further sampled for pose calculation. As a result, the randomness of sampling is reduced and the inliers for possible rigid bodies are enhanced, which guarantees the accuracy of motion estimation. On this basis, the tracking of rigid bodies is achieved. The proposed method distinguishes the feature points of dynamic objects with 3-D motion from those in the static background, thus enabling simultaneous localization and mapping (SLAM) to be initialized in dynamic environments. The experimental results on the KITTI, Hopkins 155, and MTPV62 datasets demonstrate the effectiveness. Comparison experiments indicate that our method outperforms the other methods in sensitivity of dynamic objects perception.

Determinants of Tourists’ Intention to Consume Malaysian Indigenous Cuisine

Understanding the characteristics that influence tourists to consume indigenous cuisine may aid the sector in improving service and food quality. As individuals learn about food and services, industry operators must become more strategic in dealing with tourists and ensuring they have the most incredible possible experience. This situation will indirectly affect tourists returning to certain countries for their indigenous cuisines, aiding the sector in adopting the finest promotion techniques. Therefore, this study attempts to identify the determinants of tourists’ intention to consume indigenous cuisine. This research was a content analysis study that used secondary data as primary references. All relevant information was collected, and a systematic analysis approach was used to organise the data. All data were authoritatively mandated based on each article's publishing date, authors, objective, and other related points. The results show that the quality of the food, including the taste, authenticity, safety of food, food image, price, and destination food image, were determinants affecting tourists to consume indigenous cuisine.

Impact of Pedestrian Pavement Design on the Users’ Comfort Level in an Intermodal Passenger Terminal

AbstractThe design of intermodal passenger terminals (IPTs) facilities requires a careful study of different aspects, including the walkability of their pedestrian paths. As for paved surfaces intended for pedestrians, these infrastructures must perform several tasks: greater attention is paid to functional requirements, considering different physical and perceptive people abilities. The regularity, grip and comfort are the prevailing aspects that need more attention from the designer. IPTs’ users are generally passengers performing long trips, who are more likely to carry a luggage: for this reason, the comfort and the effort to which the traveller is subjected when pulling or pushing a suitcase are significant features to analyse. Thus, an integrated approach of experimental measurements was introduced to characterise the comfort level of paved surfaces of the Mediopadana high-speed rail station (Italy), using a smart and original procedure, named stone pavement suitcase trolley test. Through these measurements the hand-arm vibrations perceived by the user, the horizontal forces required to pull or push the suitcase and the work done by the users along a real path were determined. These parameters make it possible to evaluate, from an objective point of view, the comfort of travellers on different pedestrian routes, providing useful tools for their design.

Fast Hologram Calculation Method Based on Wavefront Precise Diffraction.

In this paper, a fast hologram calculation method based on wavefront precise diffraction is proposed. By analyzing the diffraction characteristics of the object point on the 3D object, the effective viewing area of the reproduced image is analyzed. Based on the effective viewing area, the effective hologram size of the object point is obtained, and then the accurate diffraction calculation from the object point to the wavefront recording plane (WRP) is performed. By calculating all the object points on the recorded object, the optimized WRP of the whole 3D object can be obtained. The final hologram is obtained by calculating the diffraction light field from the WRP to the holographic plane. Compared with the traditional method, the proposed method can improve the calculation speed by more than 55%, while the image quality of the holographic 3D display is not affected. The proposed calculation method provides an idea for fast calculation of holograms and is expected to contribute to the development of dynamic holographic displays.

Economic Development with Environmental Protection in Ho Chi Minh City, Vietnam: Situations and Problems

Objective: The study focuses on the current status of economic development associated with environmental protection in Ho Chi Minh City. It points out the remaining aspects that must be addressed to ensure the environment is increasingly protected. Socio-economic development progress of Ho Chi Minh City, Vietnam. Methods: The study is based on the points of view and methodological principles of Marxism such as objective point of view, comprehensive point of view, development point of view, and specific historical point of view. At the same time, the research also uses specific research methods such as analysis and synthesis, comparison and contrast, history and logic, interdisciplinary research method, method of systematization, and generalization. Findings: The article proposes some solutions to realize the relationship between economic development and environmental protection in Ho Chi Minh City in the coming time. Conclusions: The study of the relationship between economic development and environmental protection in Ho Chi Minh City in general and Vietnam, in particular, is becoming increasingly urgent and important.

Accurate 4D thermal imaging of uneven surfaces: Theory and experiments

Conventional IR thermography is basically a 3D imaging model: pixel position (2D) and temperature (1D). However, the surface roughness, which cannot be depicted by the 2D geometry imaging, may introduce significant error into the temperature measurement, especially in close range. Although there are some principle studies on this theme, quantitative tools for arbitrary-shaped surfaces is still lacking. In this work, we have developed an imaging system composed of a binocular camera using structured illumination and an IR camera to reconstruct 4D uneven surface thermal images. This 4D information includes the 3D geometry of the surface and the temperature mapping over it. Based on the highly accurate surface roughness image obtained, a meshed area calculation tool is used to establish a temperature self-correction module for rough/uneven surfaces, which include convex surface, concave surface or their combinations. The effective emissivity of convex as well as flat surface is easy to calculate, because is this case, there is no inter-radiation or reflection. On contrast, the determination of concave surface emissivity is still posed as a challenge. In this work, some theoretic gap was first filled and then the experimental verification was provided. The temperature-correction takes angle of view and emissivity calibration into account. The angle of view is relative to the position of the object point and the emissivity calibration is based on the 3D geometry of the uneven surfaces. An imaginary flat surface is used to provide an effective emissivity for a local roughness. Experimental results shows that without this temperatue-correction module, the deviations between IR thermography and thermocouple are from ∼2.35 °C (at 50 °C) to ∼4.96 °C (at 300 °C) and from ∼2.43 °C (at 50 °C) to ∼4.78 °C (at 300 °C), while after the correction, the deviations are from ∼0.12 °C (at 50 °C) to ∼0.20 °C (at 300 °C) and from ∼0.16 °C (at 50 °C) to ∼0.20 °C (at 300 °C). Utilizing the thermal imaging model for recorrection, the deviations are from ∼0.13 °C (at 50 °C) to ∼0.19 °C (at 300 °C) and from ∼0.16 °C (at 50 °C) to ∼0.19 °C (at 300 °C). This generalized solution paves the way for high-accuracy temperature measurement on complexly rough surface, especially for high temperature, at which the measuring deviation can be very large.

Combined finite-element and scalar diffraction simulation of light scattering on zone edges of diffractive intraocular lenses.

A method has been developed to simulate the effects of scattered light on the image quality of optical systems. The coherent model is based on geometrical optics to take account of wavefront aberrations caused by lenses, applies finite-element calculation to solve Maxwell's equations around small scattering structures such as edges of diffractive surface zones, and uses scalar diffraction for free-space light propagation. The implementation is discussed in detail, and the operation is demonstrated on diffractive intraocular lenses. Point spread and modulation transfer functions are evaluated for an axial object point, taking account of scattered light as a function of slant angle and round radius of diffractive zone edges. Results show that, at a distance of ±200 Airy radius (i.e.,±2.1∘) from the axis, scattered irradiance is about 5 times more than without considering edge effects. Optimum round radius was found to be 7% of the step height, which agrees with simple geometrical optical estimations.

Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City.

With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.

SEGANet: 3D object detection with shape-enhancement and geometry-aware network

3D object detection approaches from point clouds develop rapidly. However, the distribution of point clouds is unbalanced in the real scene, and thus the distant or occluded objects suffer from too few points to be perceived. This case damages the overall detection accuracy. Hence, we propose a novel two-stage 3D object detection framework, the Shape-Enhancement and Geometry-Aware Network (SEGANet), which aims to mitigate the negative impact of unbalanced point distribution for boosting detection performance. In stage 1, we first capture fine-grained structural knowledge with the assistance of point-wise features from voxels to generate proposals. And in stage 2, we construct a shape enhancement module to reconstruct complete surface points for objects within proposals, then establish an elaborate geometric relevance-aware Transformer module to aggregate high-correlated feature pairs of reconstructed-known parts and decode vital geometric relations of aggregated features. Thus, critical geometric clues are supplied for objects from the data and feature levels, achieving enhanced features for box refinement. Extensive experiments on KITTI and Waymo datasets show that SEGANet achieves low model complexity and excellent detection accuracy, especially leading the baseline method by 2.18% gain in overall detection accuracy and 1.8% gain in average accuracy of weakly sensing objects. This verifies that SEGANet effectively alleviates the impact of point imbalance to significantly boost detection performance.

Perceptual Quality Assessment of Colored 3D Point Clouds.

3D point clouds have found a wide variety of applications in multimedia processing, remote sensing, and scientific computing. Although most point cloud processing systems are developed to improve viewer experiences, little work has been dedicated to perceptual quality assessment of 3D point clouds. In this work, we build a new 3D point cloud database, namely the Waterloo Point Cloud (WPC) database. In contrast to existing datasets consisting of small-scale and low-quality source content of constrained viewing angles, the WPC database contains 20 high quality, realistic, and omni-directional source point clouds and 740 diversely distorted point clouds. We carry out a subjective quality assessment experiment over the database in a controlled lab environment. Our statistical analysis suggests that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on an attention mechanism and a variant of information content-weighted structural similarity, which significantly outperforms existing PCQA models. The database has been made publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database.