Cost Map Research Articles

A comprehensive cost mapping of digital technologies in greenhouses

Conventional greenhouse producers face significant challenges in integrating advanced Industry 4.0 technologies into their production processes. One of the main obstacles is the lack of clarity regarding the components of technological costs. This article develops a cost mapping for the implementation of such technologies in the context of greenhouses. The mapping distinguishes between capital expenditures (CAPEX) and operational expenditures (OPEX), categorizing the key technological components and their financial implications. Based on the general findings from the literature review, several cost areas can be identified and classified as follows: hardware acquisition, installation and retrofitting, integration and customization, software and services, operational and maintenance costs. This cost structure will serve as a basis for future economic models and cost-benefit analysis (CBA), promoting strategic decision-making and a more informed and precise selection of digital technologies.

Techno-economic and environmental assessment of green hydrogen and ammonia production from solar and wind energy in the republic of Djibouti: A geospatial modeling approach

This study conducts a thorough economic and technical analysis to assess the viability of green hydrogen and green ammonia production using renewable energy sources in the Republic of Djibouti. We explore the economic competitiveness of utilizing wind and solar power for sustainable energy production through various measures including levelized cost of energy (LCOE), hydrogen (LCOH), and ammonia (LCOA). Our analysis incorporates sensitivity assessments and Monte Carlo simulations to predict financial feasibility and environmental impact, focusing on CO2 emission reductions. A cost mapping of electricity, green hydrogen, and green ammonia from solar and wind resources was created to find Djibouti's most cost-effective production sites. The feasibility of exporting green ammonia to neighboring countries with high fertilizer demand was also evaluated using rail and truck transport scenarios. Key findings demonstrate that Moulouhlé, endowed with robust wind and solar resources, presents a strategic site for deploying renewable energy technologies. Wind energy, with a lower LCOE of 0.066 $/kWh compared to solar's 0.093 $/kWh, emerges as a more cost-effective solution for both hydrogen and ammonia production. Notably, wind-powered hydrogen production costs are 2.25 $/kgH2, significantly lower than solar-powered costs at 4.17 $/kgH2. In terms of green ammonia, wind power achieves production costs of 399.76 $/tonNH3, outperforming solar power which stands at 537.11 $/tonNH3. Additionally, our study evaluates the logistics of exporting green ammonia, determining rail transport as a more economically viable option than trucking. This research not only underscores Djibouti's potential as a leader in green energy exportation but also aligns with global decarbonization efforts, showcasing significant CO2 savings—154.94 Mt annually from wind. This work emphasizes the importance of strategic resource utilization in Djibouti, offering insights critical for stakeholders in making informed decisions about investing in renewable energy infrastructures. The outcomes suggest a promising future for regional energy sustainability and economic resilience, fostering a transition towards low-carbon energy systems.

Autonomous Vehicles Traversability Mapping Fusing Semantic–Geometric in Off-Road Navigation

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process.

Parallel inertial forward–backward splitting methods for solving variational inequality problems with variational inclusion constraints

The inertial forward–backward splitting algorithm can be considered as a modified form of the forward–backward algorithm for variational inequality problems with monotone and Lipschitz continuous cost mappings. By using parallel and inertial techniques and the forward–backward splitting algorithm, in this paper, we propose a new parallel inertial forward–backward splitting algorithm for solving variational inequality problems, where the constraints are the intersection of common solution sets of a finite family of variational inclusion problems. Then, strong convergence of proposed iteration sequences is showed under standard assumptions imposed on cost mappings in a real Hilbert space. Finally, some numerical experiments demonstrate the reliability and benefits of the proposed algorithm.

Multi-Modality Ensemble Distortion for Spatial Steganography With Dynamic Cost Correction

This paper tackles a recent challenge in designing an efficient steganographic distortion model, whose goal is to accurately measure the modification cost of a pixel and help design steganographic schemes with high undetectability. Existing distortion models mostly assume that different modification directions of a pixel have an identical cost value and that pixel modifications are independent. These assumptions, however, may not lead to good steganography design because the modification direction of neighbouring pixels may affect the cost measurement of the current pixel. To address this problem, we propose a new distortion calculation method using dynamic cost correction and multi-modality distortion ensemble. The proposed scheme first employs a given distortion model to generate the original cost map. The cost of each pixel is then dynamically adjusted with majority voting according to the modification directions of its neighbouring pixels. Furthermore, different distortion calculation models are integrated to make the final decision on the distortion of each pixel. Experimental results show that compared to existing additive distortion-based steganographic schemes and deep learning-based steganographic schemes, steganography using our proposed distortion model performs better when tested against state-of-the-art steganalysis methods.

Serial and parallel organ-at-risk-specific noncoplanar arc optimization for small versus large target volumes in liver SBRT.

Noncoplanar arc optimization has been shown to reduce OAR doses in SRS/SRT and has the potential to reduce doses to OARs in SBRT. Extracranial targets have additional considerations, including large OARs and, in the case of the liver, volume constraints on the healthy liver. Considering pathlengths through OARs that encompass target volumes may lead to specific dose reductions as in the encompassing healthy liver tissue. These optimizations must also leverage delivery efficiency and trajectory sampling to ensure ease of clinical translation. The purpose of this research is to generate optimized static-couch arcs that separately consider serial and parallel OARs and arc delivery efficiency, with a trajectory sampling metric, towards the aim of reducing dose to OARs and the surrounding healthy liver tissue. Separate BEV cost maps were created for parallel, and serial OARs by means of a fast ray-triangle intersection algorithm. An additional BEV cost map was created for the liver which, by definition, encompasses the liver tumors. The individual costs of these maps were summed and combined with the sampling metric for 100 000 random combinations of arc trajectories. A search algorithm was applied to find an arc trajectory solution that satisfied BEV cost and sampling optimization, while also ensuring an efficient delivery was possible with a low number of arcs. This method of arc selection was evaluated for 16 liver SBRT patients characterized by small and large target volumes. Comparisons were made with a clinical arc template of coplanar arcs. Dosimetric plan quality was evaluated using published guidelines and metrics from RTOG1112. Four of five plan quality metrics for the liver were significantly reduced when planned with optimized noncoplanar arcs. Median (range) reductions of the volumes receiving 10, 18, and 21Gy were found of 140.4 (295.8) cc (p=0.001), 28.2 (230.6) cc (p=0.002) and 18.5 (155.5) cc (p=0.04). A significant increase in median (range) dose to the right kidney of 0.2±0.9Gy (p=0.03) was also found using optimized noncoplanar arcs, which was below the tolerance of 10Gy for all cases. The average number of arcs chosen was 4±1. Optimizing serial and parallel OARs separately during static couch noncoplanar arc selection significantly reduced the dose to the liver during SBRT using a moderate number of arcs.

Long-term navigation for autonomous robots based on spatio-temporal map prediction

The robotics community has witnessed a growing demand for long-term navigation of autonomous robots in diverse environments, including factories, homes, offices, and public places. The core challenge in long-term navigation for autonomous robots lies in effectively adapting to varying degrees of dynamism in the environment. In this paper, we propose a long-term navigation method for autonomous robots based on spatio-temporal map prediction. The time series model is introduced to learn the changing patterns of different environmental structures or objects on multiple time scales based on the historical maps and forecast the future maps for long-term navigation. Then, an improved global path planning algorithm is performed based on the time-variant predicted cost maps. During navigation, the current observations are fused with the predicted map through a modified Bayesian filter to reduce the impact of prediction errors, and the updated map is stored for future predictions. We run simulation and conduct several weeks of experiments in multiple scenarios. The results show that our algorithm is effective and robust for long-term navigation in dynamic environments.

Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

SummaryMobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).

The neural oscillations serving task switching are altered in cannabis users.

Regular cannabis is known to impact higher-order cognitive processes such as attention, but far less is known regarding cognitive flexibility, a component of executive function. Moreover, whether such changes are related to aberrations in the neural oscillatory dynamics serving flexibility remains poorly understood. Quantify the neural oscillatory dynamics serving cognitive flexibility by having participants complete a task-switching paradigm during magnetoencephalography (MEG). Probe whole-brain maps to identify alterations in chronic cannabis users relative to nonusers and determine how these alterations relate to the degree of cannabis use involvement. In all, 25 chronic cannabis users and 30 demographically matched nonuser controls completed neuropsychological testing, an interview regarding their substance use, a urinalysis, and a task switch paradigm during MEG. Time-frequency windows of interest were identified using a data-driven statistical approach and these were imaged using a beamformer. Whole-brain neural switch cost maps were computed by subtracting the oscillatory maps of the no-switch condition from the switch condition per participant. These were examined for group differences. Cannabis users had weaker theta switch cost responses in the dorsolateral and dorsomedial prefrontal cortices, while nonusers showed the typical pattern of greater recruitment during switch relative to no switch trials. In addition, theta activity in the dorsomedial prefrontal cortex was significantly correlated with cannabis use involvement. Cannabis users exhibited altered theta switch cost activity compared to nonusers in prefrontal cortical regions, which are critical for cognitive flexibility. This activity scaled with cannabis use involvement, indicating a link between cannabis use and aberrant oscillatory activity underlying cognitive flexibility.

SatellStitch: Satellite Imagery-Assisted UAV Image Seamless Stitching for Emergency Response without GCP and GNSS

Rapidly stitching unmanned aerial vehicle (UAV) imagery to produce high-resolution fast-stitch maps is key to UAV emergency mapping. However, common problems such as gaps and ghosting in image stitching remain challenging and directly affect the visual interpretation value of the imagery product. Inspired by the data characteristics of high-precision satellite images with rich access and geographic coordinates, a seamless stitching method is proposed for emergency response without the support of ground control points (CGPs) and global navigation satellite systems (GNSS). This method aims to eliminate stitching traces and solve the problem of stitching error accumulation. Firstly, satellite images are introduced to support image alignment and geographic coordinate acquisition simultaneously using matching relationships. Then a dynamic contour point set is constructed to locate the stitching region and adaptively extract the fused region of interest (FROI). Finally, the gradient weight cost map of the FROI image is computed and the Laplacian pyramid fusion rule is improved to achieve seamless production of the fast-stitch image map with geolocation information. Experimental results indicate that the method is well adapted to two representative sets of UAV images. Compared with the Laplacian pyramid fusion algorithm, the peak signal-to-noise ratio (PSNR) of the image stitching results can be improved by 31.73% on average, and the mutual information (MI) can be improved by 19.98% on average. With no reliance on CGPs or GNSS support, fast-stitch image maps are more robust in harsh environments, making them ideal for emergency mapping and security applications.

A novel Human-Aware Navigation algorithm based on behavioral intention cognition

AbstractIn order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a “first-come, first-served” expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments.

A low-cost regularization strategy for the 3D reconstruction of wind turbines based on remote sensing images

ABSTRACT Unmanned aerial vehicle remote sensing images are easy to acquire and cost-effective. Their application provides a new approach for the maintenance of offshore wind farms. To obtain an accurate model of wind turbines, thereby improving detection efficiency and mitigating the risks associated with high-altitude workings, a low-cost method for wind turbine three-dimensional (3D) reconstruction is proposed. The main advantages of deep learning-based Multi-View Stereo Network (MVSNet) include high accuracy, fast processing speed, and ease of extension to multiple viewpoints. Most networks utilize multi-scale 3D Convolutional Neural Network (CNN) for regularization. However, when dealing with images that have high depth and resolution, CNN regularization strategy leads to a significant increase in memory usage. In this paper, the two-dimensional cost maps are regularized along the depth dimension sequentially using Stacked Convolutional Gated Recurrent Unit (Stacked Conv-GRU), instead of reducing the 3D cost volume all at once. Localized convolution operations replace undifferentiated fully connected operations, substantially reducing memory consumption and enabling high-resolution reconstruction. We conduct comparisons and model evaluations on the ”Nordtank” wind turbine remote sensing image dataset. Experimental results demonstrate that, compared to other 3D reconstruction methods, Stacked Conv-GRU regularized MVSNet reduces run time and hardware requirements while maintaining similar accuracy.

STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds

This article proposes a means of autonomous mobile robot navigation in dense crowds based on predicting pedestrians’ future trajectories. The method includes a pedestrian trajectory prediction for a running mobile robot and spatiotemporal path planning for when the path crosses with pedestrians. The predicted trajectories are converted into a time series of cost maps, and the robot achieves smooth navigation without dodging to the right or left in crowds; the path planner does not require a long-term prediction. The results of an evaluation implementing this method in a real robot in a science museum show that the trajectory prediction works. Moreover, the proposed planning’s arrival times is 26.4% faster than conventional 2D path planning’s arrival time in a simulation of navigation in a crowd of 50 people.

Bi‐objective Trade‐Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy–Pareto Algorithm for a Multimode Hybrid Electric Vehicle

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV.

Heuristic dynamic path planning algorithm based on SALSTM-DDPG

In the field of path planning, there are many excellent traditional algorithms proposed by scholars, but usually these traditional methods based on static environment, these methods lack the ability to deal with dynamic variable environment. A heuristic reinforcement learning dynamic environment path planning algorithm (SALSTM-DDPG) combining self-attention and long short-term memory network(LSTM) is proposed in this paper. Firstly, an automatic encoder is constructed to reduce the feature dimension of the local cost map, which reduces the complexity of the overall model. Then the local cost map and global path after dimensionality reduction are taken as input, the original source of information is guaranteed to the maximum extent possible by this method, at the same time, the global optimal path is used to guide the local path planning. Finally, the deep reinforcement learning algorithm DDPG is used for specific motion control, the Actor part is built using a network based on the SALSTM algorithm. This approach allows previous sequence to be used as a reference when an Actor makes decisions, this enables dynamic obstacles to be predictably avoided by the robot. The feasibility and efficiency of the proposed algorithm are proved by experiments.

THE RELATIONSHIP BETWEEN RISK AND COST OF QUALITY: THE CASE OF JESSE LANGFORD

Risk permeates every moment of life, impacting both individuals and businesses through their activities. While certain risks encountered have a minimal likelihood of materializing, others possess a considerable probability. Preventing risks in life entails averting failures. The most effective approach to preventing failures, both in business and all aspects of life, is through proactive measures that eliminate the underlying causes of failure before they occur. Engaging in prevention activities is invariably more cost-effective than dealing with the potential consequences and expenses associated with failures. In situations where the risk's realization probability is high, preventive measures can be implemented to mitigate or prevent it altogether. Consequently, by actively addressing risks, failures can be averted through risk prevention. This study aims to conduct a comparative analysis of the concepts of risk and quality within the realm of costs while introducing the concept of risk-quality cost mapping to the existing body of literature. Within this framework, the study initially establishes a connection between the elements of the risk management process and quality costs. Subsequently, a real-life case is examined to assess its implications in terms of both risk and quality costs. As can be seen in the case of Jesse Langford, it is clear that the implementation of comprehensive prevention measures plays a crucial role in minimizing failure costs, ultimately reducing them to a negligible level during travel. As a result of preventive measures, the costs associated with failures can be avoided, the impact of risks can be reduced, and in some cases, the costs of failure can be substantially eliminated.

A heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN-based position posture map and rapidly-exploring random tree

The environment of tomato bunches is complex, and the fruit volume is relatively large, so manipulator picking-harvest motion planning should consider not only how to pick, but also how to avoid obstacles after picking tomato bunches and harvesting them from the complex environment, which puts forward strict requirements for picking-harvest motion performance. However, existing sampling-based algorithms still have problems of blind expansion and low efficiency. To solve these problems, a heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN (convolution neural network)-based position posture map (PPM) and rapidly-exploring random tree (RRT) is proposed in this paper. This method comprehensively considers the sampling effectiveness of the whole process of picking and harvesting, search speed, path cost and manipulability of the manipulator, combining a sampling-based path planning algorithm and deep learning framework. The method utilizes CNN and FCN(Fully Connected Neural Networks) for generating a heuristic cost map where each voxel has a cost-to-go value toward the target and an optimal target posture value to guide the expansion tree. In addition, the sampling space of obstacle avoidance planning is reduced by a spatial partition method, and the difficulty caused by the increase in the volume of the end-effector clamping fruit is solved by prioritizing the planning of the fruit harvest path and then planning the picking path. On the basis of the above research, a large number of harvest experiments have verified the effectiveness of the proposed algorithm. Research and experimental results show that, based on the PPM-RRT algorithm, the picking-harvest time of a single tomato bunch is 13 s, and the success rate is close to 100%. Compared with state-of-the-art sampling algorithms, the planning time is reduced by more than 70%, the path length is reduced by more than 24%, and the manipulability of the manipulator is increased by 38.9%. The proposed path planning method is effective in complex environments.

Suitable sites for groundwater development: a capture map-based approach integrated with weighted overlay analysis

Abstract A novel methodology for suitable site selection for groundwater development based on river capture, pumping cost, and groundwater potential has been proposed for better groundwater utilisation. River capture and cost map have been generated from a calibrated groundwater model, simulated with forecasted hydrological time series data. The groundwater potential has been calculated with weighted overlay analysis. These three variables have been used to classify the model domain into five zones of groundwater development by K-Means clustering. The area with lower river capture, low cost of pumping, and high groundwater potential is found to be the best location for groundwater extraction. The methodology has been applied to the lower Ain River basin, in France.

Novel cartographer using an OAK-D smart camera for indoor robots location and navigation

In recent years, service robots have been widely used in people’s daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cloud matching. Then a software system is realized by integrating each module. The experiment proves that the system is practical and effective, and has practical value.

Convergence of the projection and contraction methods for solving bilevel variational inequality problems

In this work, we analyze some convergent properties of a projection and contraction algorithm for solving a variational inequality problem, where the feasible domain is the solution set of an affine variational inequality problem. We prove that, for solving the problem where the second cost mapping is affine and not necessary for monotone properties, any iterative sequence generated by the algorithm converges to a unique solution provided that the first cost mapping is strongly monotone and Lipschitz continuous. Computational errors of the algorithm are showed. Finally, some preliminary numerical experiences and comparisons are also reported.