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Articles 5491 - 5520 of 11187

Full-Text Articles in Artificial Intelligence and Robotics

Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung Mar 2022

Riconv++: Effective Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene understanding tasks. While recent works show that point cloud convolutions can be invariant to translation and point permutation, investigations of the rotation invariance property for point cloud convolution has been so far scarce. Some existing methods perform point cloud convolutions with rotation-invariant features, existing methods generally do not perform as well as translation-invariant only counterpart. In this work, we argue that a key reason is that compared to …

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Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations, Timothy Maninger, Daniel Burton Shank Mar 2022

Perceptions Of Violations By Artificial And Human Actors Across Moral Foundations, Timothy Maninger, Daniel Burton Shank

Psychological Science Faculty Research & Creative Works

Artificial agents such as robots, chatbots, and artificial intelligence systems can be the perpetrators of a range of moral violations traditionally limited to human actors. This paper explores how people perceive the same moral violations differently for artificial agent and human perpetrators by addressing three research questions: How wrong are moral foundation violations by artificial agents compared to human perpetrators? Which moral foundations do artificial agents violate compared to human perpetrators? What leads to increased blame for moral foundation violations by artificial agents compared to human perpetrators? We adapt 18 human-perpetrated moral violation scenarios that differ by the moral foundation …

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Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner Mar 2022

Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner

Theses and Dissertations

Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. …

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Evaluating Semantic Matching Techniques For Technical Documents, Rain F. Dartt Mar 2022

Evaluating Semantic Matching Techniques For Technical Documents, Rain F. Dartt

Theses and Dissertations

Machine learning models that employ NLP techniques have become more widely accessible, making them an attractive solution for text and document classification tasks traditionally accomplished by humans. Two such use cases are matching the specialized experience required for a job to statements in applicant resumes, and finding and labelling clauses in legal contracts The AFMC has an immediate need for solutions to civilian hiring. However, there is currently no truth data to validate against. A similar task is contract understanding for which there is the CUAD, a recently published repository of 510 contracts manually labelled by legal experts. The presented …

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Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell Mar 2022

Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell

Theses and Dissertations

The United States Air Force is investing in artificial intelligence (AI) to speed analysis in efforts to modernize the use of autonomous unmanned combat aerial vehicles (AUCAVs) in strike coordination and reconnaissance (SCAR) missions. This research examines an AUCAVs ability to execute target strikes and provide reconnaissance in a SCAR mission. An orienteering problem is formulated as anMarkov decision process (MDP) model wherein a single AUCAV must optimize its target route to aid in eliminating time-sensitive targets and collect imagery of requested named areas of interest while evading surface-to-air missile (SAM) battery threats imposed as obstacles. The AUCAV adjusts its …

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Obsolescence: Evaluating An Educational Serious Game On Artificial Intelligence Impacts To Military Strategic Goals, Timothy C. Kokotajlo Mar 2022

Obsolescence: Evaluating An Educational Serious Game On Artificial Intelligence Impacts To Military Strategic Goals, Timothy C. Kokotajlo

Theses and Dissertations

Artificial Intelligence (AI) threatens to bring significant disruption to all aspects of military operations. This research develops a Serious Game (SG) and assessment methodology to provide education on the mindsets required for engaging with disruptive AI technologies. The game, Obsolescence, teaches strategic-level concepts recommended to the Department of Defense (DoD) from a compilation of reports on the current and future state of AI and warfighting. The methodology for assessing the educational value of Obsolescence addresses common challenges such as subjective reporting, control groups, population sizes, and measuring abstract or high levels of learning. The games proposed educational value is tested …

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Coupled Orbit-Attitude Dynamics And Control Of A Cubesat Equipped With A Robotic Manipulator, Charles M. Carr Mar 2022

Coupled Orbit-Attitude Dynamics And Control Of A Cubesat Equipped With A Robotic Manipulator, Charles M. Carr

Theses and Dissertations

This research investigates the utility and expected performance of a robotic servicing CubeSat. The coupled orbit-attitude dynamics of a 6U CubeSat equipped with a four-link serial manipulator are derived. A proportional-integral-derivative controller is implemented to guide the robot through a series of orbital scenarios, including rendezvous and docking following ejection from a chief spacecraft, repositioning the end effector to a desired location, and tracing a desired path with the end effector. Various techniques involving path planning and inverse differential kinematics are leveraged. Simulation results are presented and performance metrics such as settling time, state errors, control use, and system robustness …

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Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper Mar 2022

Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper

Theses and Dissertations

This research documents architecture and implementation of an enhanced interference detection and classification analysis system, using both a database and storage solution utilizing machine learning algorithms to detect changes in Carrier-to-Noise strength over multiple GNSS sites. The system uses publicly-available government supported receivers to detect interference, and built using FOSS packaged as a programming library through Python. Two algorithms are discussed in terms of enhancing interference detection using both non-machine learning and machine learning approaches. Two algorithms are also discussed which are used for classification of events. In addition, an approach to Large Scale data analytics is demonstrated via a …

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Automated Aircraft Visual Inspection With Artificial Data Generation Enabled Deep Learning, Nathan J. Gaul Mar 2022

Automated Aircraft Visual Inspection With Artificial Data Generation Enabled Deep Learning, Nathan J. Gaul

Theses and Dissertations

Aircraft visual inspection, which is essential to daily maintenance of an aircraft, is expensive and time-consuming to perform. Augmenting trained maintenance technicians with automated UAVs to collect and analyze images for aircraft inspection is an active research topic and a potential application of CNNs. Training datasets for niche research topics such as aircraft visual inspection are small and challenging to produce, and the manual process of labeling these datasets often produces subjective annotations. Recently, researchers have produced several successful applications of artificially generated datasets with domain randomization for training CNNs for real-world computer vision problems. The research outlined herein builds …

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Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej Mar 2022

Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej

Theses and Dissertations

This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.

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Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire Mar 2022

Human-Algorithm Collaboration Works Best If Humans Lead (Because It Is Fair!), David De Cremer, Jack Mcguire

Research Collection Lee Kong Chian School Of Business

Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n …

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Design Demand Trend Acquisition Method Based On Short Text Mining Of User Comments In Shopping Websites, Zhiyong Xiong, Zhaoxiong Yan, Huanan Yao, Shangsong Liang Feb 2022

Design Demand Trend Acquisition Method Based On Short Text Mining Of User Comments In Shopping Websites, Zhiyong Xiong, Zhaoxiong Yan, Huanan Yao, Shangsong Liang

Machine Learning Faculty Publications

In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and …

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An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub Feb 2022

An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub

Computer Vision Faculty Publications

Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Tradi-tional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diag-nosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient med-ical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and …

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Job Scheduling And Simulation In Cloud Based On Deep Reinforcement Learning, Qirui Li, Xinyi Peng Feb 2022

Job Scheduling And Simulation In Cloud Based On Deep Reinforcement Learning, Qirui Li, Xinyi Peng

Journal of System Simulation

Abstract: To solve the difficulty in job scheduling in the complex and transient multi-user, multi-queue, and multi-data-center cloud computing environment, this paper proposed a job scheduling method based on deep reinforcement learning. A system model of cloud job scheduling and its mathematical model were built, and an optimization goal consisting of transmission time, waiting time, and execution time was obtained. A job scheduling algorithm based on deep reinforcement learning was designed, and its state space, action space, and reward function were given. A simulated cloud job scheduler was designed and developed, and simulated scheduling experiments were conducted on it. The …

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Multi-Floor Evacuation Model Based On Wavelet Neural Network, Juan Wei, Lei You, Yangyong Guo, Zhihai Tang Feb 2022

Multi-Floor Evacuation Model Based On Wavelet Neural Network, Juan Wei, Lei You, Yangyong Guo, Zhihai Tang

Journal of System Simulation

Abstract: Crowd evacuation in a multi-floor environment is a popular social concern, while the stagnation phenomenon easily occurs when simulating a multi-floor complex environment with the traditional social force model. Therefore, An improved social force model is proposed by a wavelet neural network, and a new multi-floor evacuation model is built. In the model, a pedestrian's direction of movement is obtained by the field model, which is used as the self-driving direction of the social force model. Meanwhile, the evaluation indexes of the exit congestion degree, path congestion degree, and average velocity in a multi-floor environment are given, and a …

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Research On Semantic Segmentation Of Natural Landform Based On Edge Detection Module, Qizong Shen, Chunyan Gao Feb 2022

Research On Semantic Segmentation Of Natural Landform Based On Edge Detection Module, Qizong Shen, Chunyan Gao

Journal of System Simulation

Abstract: To classify pixels of natural landform edges in remote sensing images, this paper proposes a multi-channel fusion model and a decoder-side module model both integrating an edge detection module. The edge detection module takes the Canny operator as the base to perform closed operations and mean filtering, as a result of which accurate image edges can be achieved. Based on DeepLabV3+, the semantic segmentation network is connected with an edge planning module in parallel at encoder and decoder sides respectively. The experimental results show that the two improved networks can achieve a better segmentation effect on a high-resolution natural …

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Zoomfft-Based Demodulation Algorithm For Underwater Acoustic Ofdm Signals, Qing Guo, Angdi Li, Jing Wu, Haitao Su Feb 2022

Zoomfft-Based Demodulation Algorithm For Underwater Acoustic Ofdm Signals, Qing Guo, Angdi Li, Jing Wu, Haitao Su

Journal of System Simulation

Abstract: The picket fence effect of fast Fourier transform (FFT) restricts the demodulation performance of the underwater acoustic(UWA) communication systemusing orthogonal frequency division multiplexing (OFDM). To solve this problem, we propose a demodulation algorithm based on ZoomFFT. Specifically, the received signal is processed by frequency shifting and downsampling forarefined spectrum, which improves the spectralresolution and weakens the picketfence effect.Meanwhile, the channel response is refined, and the channel equalization algorithm is constructed on the basis of the minimum mean square error (MMSE) principle to eliminate the channel influence. Simulations show that the performance of underwater acoustic OFDM demodulation algorithm based on …

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Design Of Fatigue Driving Detection System Based On Cascaded Neural Network, Bangqian Ao, Sha Yang, Jinqing Linghu, Zhenhuan Ye Feb 2022

Design Of Fatigue Driving Detection System Based On Cascaded Neural Network, Bangqian Ao, Sha Yang, Jinqing Linghu, Zhenhuan Ye

Journal of System Simulation

Abstract: An algorithm is proposed to greatly improves the face detection rate and ensures the accuracy by adjusting the size of input images, expanding the minimum face size, and reducing the scaling ratio between layers of the detection window. The detection efficiency of this algorithm is 18 times higher than that of the original MTCNN. By building a new CNN structure model for the detection of eyes and mouths, we can achieve network detection accuracy of 95.6%. The proposed network is cascaded with the original MTCNN to continue classifying and locating the eyes and mouth in the formerly …

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Segmentation Line Detection In Dental Model Based On Target Region Constraint, Tian Ma, Yun Li, Jiaojiao Li, Yuancheng Li Feb 2022

Segmentation Line Detection In Dental Model Based On Target Region Constraint, Tian Ma, Yun Li, Jiaojiao Li, Yuancheng Li

Journal of System Simulation

Abstract: It is an important pretreatment of a virtual orthodontic system to accurately segment teeth from a dental model. In the present methods, all patches are computed directly. To solve this problem, this paper proposes a segmentation line detection method based on target region constraint, which narrows down the detection range to the area around the actual segmentation line. In this method, the cutting plane and the cutting line are automatically formed according to the positions of seed points. The detection range is determined by the search for the position with the greatest negative curvature on the cutting line. The …

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Modeling & Simulation Based System Of Systems Engineering, Lin Zhang, Kunyu Wang, Yuanjun Laili, Lei Ren Feb 2022

Modeling & Simulation Based System Of Systems Engineering, Lin Zhang, Kunyu Wang, Yuanjun Laili, Lei Ren

Journal of System Simulation

Abstract: To accommodate the dark and unstructured underwater working environment, the near-body pressure distribution characteristics of a bionic robot fish undulating in near wall region is studied. The feasibility of using artificial lateral line(ALL) to estimate the wall effect and flow field parameters is analyzed theoretically. A CFD(computational fluid dynamics) coupled solution model for near-body pressure simulation of a bionic robotic fish swimming near the wall is established and a near-body pressure data extraction and processing method is proposed. The effect of the wall clearance, inlet flow velocity and strouhal number (St) on the fish near-body pressure distribution …

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Constructing The Agent Discrete Simulation Based On Devs Atomic Model, Xiaohan Wang, Lin Zhang, Yuanjun Laili, Kunyu Xie, Tingchun Hu Feb 2022

Constructing The Agent Discrete Simulation Based On Devs Atomic Model, Xiaohan Wang, Lin Zhang, Yuanjun Laili, Kunyu Xie, Tingchun Hu

Journal of System Simulation

Abstract: In order to resolve the nonlinear and ill-posed inverse problem of the image reconstruction of electrical capacitance tomography (ECT), an image reconstruction algorithm based on one-dimensional convolutional neural network (1D CNN) is presented. The nonlinear mapping relationship between the independent measurement value of ECT system and the gray value of reconstructed image is established by 1D CNN. Six typical flow regimes with random distribution are obtained by the finite element simulation software and a 1D CNN is successfully trained. Simulation and static experiments are carried out and the reconstructed images using linear back projection, Landweber iterative algorithm and 1D …

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Transmission Line Operation And Inspection Training Simulation Based On Multiple Time Scales And Vr, Jiawen Yan, Jijie Huang, Lie Zhou, Changjin Chen, Qiang Wu, Jintao Zhao Feb 2022

Transmission Line Operation And Inspection Training Simulation Based On Multiple Time Scales And Vr, Jiawen Yan, Jijie Huang, Lie Zhou, Changjin Chen, Qiang Wu, Jintao Zhao

Journal of System Simulation

Abstract: Social learning is defined as the process that consumers use online reviews to fetch more precise information about product quality.Consequently, consumers would be more likely to purchase the product if the product quality learned was higher than their expectation, reference point effect named.To understand the impact of this effect in social learning on a firm's product decisions, we built a multi-agent model to solve the problem through simulation. According to the results, the reference point effect has a negative influence on the firm. The firm has to higher the product quality and price and therefore loses some profits. …

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Product Decisions In Presence Of Social Learning And Reference Point Effect, Feng Li, Ying Wei Feb 2022

Product Decisions In Presence Of Social Learning And Reference Point Effect, Feng Li, Ying Wei

Journal of System Simulation

Abstract: In order to find out the source of VOCs(volatile organic compounds) emission and diffusion to the target area, and prevent the target area from further pollution, this paper propose an analytical method of VOCs hazard causes in related areas based on object function Petri net. The net structure describes the relationship between the potential pollution sources and the target area, and the operation of the net system reflects the change of VOCs hazard degree in the target area, and the calculation of hazard degree is integrated into the operation of the Petri net system. Through the actual case study, …

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Review Of System Of Systems Combat Effectiveness Evaluation And Optimization Methods, Ziwei Zhang, Qisheng Guo, Zhiming Dong, Ang Gao, Yifei Wang Feb 2022

Review Of System Of Systems Combat Effectiveness Evaluation And Optimization Methods, Ziwei Zhang, Qisheng Guo, Zhiming Dong, Ang Gao, Yifei Wang

Journal of System Simulation

Abstract: The characteristics of system-of-systems combat effectiveness evaluation and optimization are analyzed. In light of the "holism", this paper proposes an idea of dividing system of systems combat effectiveness evaluation and optimization into three stages of comprehensive evaluation, analysis, and optimization. As for the practical problems that need to be solved in the three stages, typical methods suitable for each stage are summarized.The advantages and disadvantages of different methods are then compared. In view of the practical difficulties in implementing system of systems combat effectiveness evaluation and optimization guided by the "holism", this paper puts forward the next research directions …

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Simulation And Optimization Of A New Multi-Channel Contact Center System, Junxiang Li, Lichao Li, Kun Ma Feb 2022

Simulation And Optimization Of A New Multi-Channel Contact Center System, Junxiang Li, Lichao Li, Kun Ma

Journal of System Simulation

Abstract: In the actual operation of a contact center, it often encounters a large number of calls caused by an emergency. A traditional call center with a simple first-come-first-served queuing rule can hardly handle the rapid increase of calls properly. In this regard, without changing the number of seats, the call-back service channel for evacuating tasks and the special service channel for ensuring the service level are added. Considering customer abandonment, a new multi-channel queuing model for a contact center is built. This model is simulated by FlexSim software, and the results are comparatively analyzed. It is found that …

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Modeling And Optimization For Manufacturing Cell Scheduling Based On Improved Wolf Pack Algorithm And Simulation, Zi'an Zhao, Hong Zhou, Yingjian Lei Feb 2022

Modeling And Optimization For Manufacturing Cell Scheduling Based On Improved Wolf Pack Algorithm And Simulation, Zi'an Zhao, Hong Zhou, Yingjian Lei

Journal of System Simulation

Abstract: Aiming at the domestic aircraft stall spin simulation training need, a stall spin simulation training system is developed. The training system consists of the multi-channel dome visual system, the semi-physical simulation cockpit and the maneuvering force control loading system, etc. Distributed simulation technology is used to develop a realistic man-in-the-loop simulation training environment. For the stall spin simulation, the multi-source aerodynamic data is processed comprehensively, and an unsteady aerodynamic model at high angle of attack (AOA) is constructed, and the heavy-load digital electric control loading technology is used to realize the simulation of stall spin alternating force and jitter …

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Vehicle Routing Problem With Refined Oil Secondary Distribution Considering Workload Balance, Zhenping Li, Guang Yang, Qianqian Han Feb 2022

Vehicle Routing Problem With Refined Oil Secondary Distribution Considering Workload Balance, Zhenping Li, Guang Yang, Qianqian Han

Journal of System Simulation

Abstract: The small-signal model of a DC/DC converter is usually built by the analytic method, and its accuracy is verified by the frequency domain method. A new idea is developed to model the small signal of the converter, i.e., directly using the frequency domain method.The design process is as follows: The principle and modeling mechanism of the frequency domain method are analyzed, and the realization flow of Matlab modeling is introduced. A typical phase-shifted full-bridge converter is taken as the design object, and a simulation model is built by Simulink. The transfer function is obtained on the basis of …

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Lod Modeling Method For Three-Dimensional Objects With Energy Operator, Yongzhi Wang, Zhenchao Li, Pengyu Liu, Hui Wang Feb 2022

Lod Modeling Method For Three-Dimensional Objects With Energy Operator, Yongzhi Wang, Zhenchao Li, Pengyu Liu, Hui Wang

Journal of System Simulation

Abstract: Current level of detail (LOD) modeling methods do not combine simplification and subdivision, which results in models not being rich in detail level. Therefore, the energy operator was used to combine the simplification algorithm of a three-dimensional (3D) object model with the subdivision algorithm, and a LOD modeling method based on an energy operator was proposed for 3D objects. The method involves three major steps: calculation of energy operators for 3D models, model simplification and model subdivision based on an energy operator. Experiments show that this method can generate models with rich levels of detail and has good visualization …

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Monocular Semantic Slam Method Based On Object Relation Description, Shiqi Lin, Jikai Wang, Haoyuan Pei, Hao Zhao, Zonghai Chen Feb 2022

Monocular Semantic Slam Method Based On Object Relation Description, Shiqi Lin, Jikai Wang, Haoyuan Pei, Hao Zhao, Zonghai Chen

Journal of System Simulation

Abstract: Semantic information perception of the external environment and accurate positioning are the keys to autonomous navigation and operation of mobile robots. This paper proposes a method of semantic simultaneous localization and mapping (SLAM) based on a monocular camera. The system completes three-dimensional (3D) object detection while estimating the trajectory. We model the 3D objects with cuboids. Then, the semantic meanings, color distribution, size and neighborhood topology of the objects are extracted as descriptors for the accurate matching of objects between different frames. The camera pose, map points and object landmarks are optimized jointly in the backend of the system. …

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Research On Nonlinear Evaluation Method Of Situational Hot Spots, Tengjiao Mao, Dongge Zhang, Xuefeng Liang, Yanjie Niu, Minggang Yu, Ming He Feb 2022

Research On Nonlinear Evaluation Method Of Situational Hot Spots, Tengjiao Mao, Dongge Zhang, Xuefeng Liang, Yanjie Niu, Minggang Yu, Ming He

Journal of System Simulation

Abstract: The excessive amount of data and information in the situation map is likely to produce a huge cognitive load that exceeds the physiological limit. This can lead to delays in the perception, judgment, and decision-making of the commander, or even failure of decision-making activities in severe cases. For this reason, situation information needs to be processed to assess and highlight situational hotspots so that cognitive overload can be tackled. Based on the aggregation factors of collaborative targets, this paper takes the effective impact possibility of situational targets and the collaboration as the indicators, derives the attention function, and designs …

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