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Cheng, Yihua, Xu Zhang, and Junchen Jiang. "Enabling Perception-Driven Optimization in Networking." ACM SIGMETRICS Performance Evaluation Review 51, no. 2 (September 28, 2023): 103–5. http://dx.doi.org/10.1145/3626570.3626608.
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Service providers struggle to catch up with the rapid growth in bandwidth and latency demand of Internet videos and other applications. An essential contributor to this resource contention is the assumption that users are equally sensitive to service quality everywhere, so any low-quality incidents must be avoided. However, this assumption is not true. For example, our work and other parallel efforts have shown that more video users can be served with better quality of experience (QoE) if we embrace the fact that the QoE's sensitivity to video quality varies greatly with the video content. To unleash such benefits, the application systems must be driven by not only system measurement data but also user feedback data that capture users' perceptions of service quality. In this short paper, I will highlight some of our recent efforts toward the efficient collection of user feedback and enabling perception-driven optimization for Internet applications.
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P, Dr Periasamy, and Dr Dinesh N. "Data Driven Marketing Strategic Trends in 2022." International Journal of Science, Engineering and Management 9, no. 6 (June 13, 2022): 24–27. http://dx.doi.org/10.36647/ijsem/09.06.a005.
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This paper is an attempt to find out the significant data driven marketing strategic trends. The author has developed a model which will help the readers to under the data driven marketing strategies in the recents trends in the world very easily. This model is called EPCMASQ , a model of the author, That is Ethical Information, Personalized Marketing Automation, Better Customer Experience, Multi-channel Experience, Artificial Intelligence, Search Engine Optimization and Qualitative Data. With which the concept of data driven marketing strategies explained well.
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Shailesh, K. S., and P. V. Suresh. "Performance Driven Development Framework for Web Applications." Global Journal of Enterprise Information System 9, no. 1 (May 5, 2017): 75. http://dx.doi.org/10.18311/gjeis/2017/15870.
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The performance of web applications is of paramount importance as it can impact end-user experience and the business revenue. Web Performance Optimization (WPO) deals with front-end performance engineering. Web performance would impact customer loyalty, SEO, web search ranking, SEO, site traffic, repeat visitors and overall online revenue. In this paper we have conducted the survey of state of the art tools, techniques, methodologies of various aspects of web performance optimization. We have identified key web performance patterns and proposed novel web performance driven development framework. We have elaborated on various techniques related to different phases of web performance driven development framework.
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SONG, WEI, DIAN TJONDRONEGORO, and MICHAEL DOCHERTY. "EXPLORATION AND OPTIMIZATION OF USER EXPERIENCE IN VIEWING VIDEOS ON A MOBILE PHONE." International Journal of Software Engineering and Knowledge Engineering 20, no. 08 (December 2010): 1045–75. http://dx.doi.org/10.1142/s0218194010005067.
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Compared with viewing videos on PCs or TVs, mobile users have different experiences in viewing videos on a mobile phone due to different device features such as screen size and distinct usage contexts. To understand how mobile user's viewing experience is impacted, we conducted a field user study with 42 participants in two typical usage contexts using a custom-designed iPhone application. With user's acceptance of mobile video quality as the index, the study addresses four influence aspects of user experiences, including context, content type, encoding parameters and user profiles. Accompanying the quantitative method (acceptance assessment), we used a qualitative interview method to obtain a deeper understanding of a user's assessment criteria and to support the quantitative results from a user's perspective. Based on the results from data analysis, we advocate two user-driven strategies to adaptively provide an acceptable quality and to predict a good user experience, respectively. There are two main contributions from this paper. Firstly, the field user study allows a consideration of more influencing factors into the research on user experience of mobile video. And these influences are further demonstrated by user's opinions. Secondly, the proposed strategies — user-driven acceptance threshold adaptation and user experience prediction — will be valuable in mobile video delivery for optimizing user experience.
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Wu, Xiaojing, Zijun Zuo, and Long Ma. "Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs." Aerospace 9, no. 10 (October 17, 2022): 610. http://dx.doi.org/10.3390/aerospace9100610.
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The surrogate-assisted optimization (SAO) process can utilize the knowledge contained in the surrogate model to accelerate the aerodynamic optimization process. The use of this knowledge can be regarded as the primary form of intelligent optimization design. However, there are still some difficulties in improving intelligent design levels, such as the insufficient utilization of optimization process data and optimization parameters’ adjustment that depends on the designer’s intervention and experience. To solve the above problems, a novel aerodynamic data-driven surrogate-assisted teaching-learning-based optimization (TLBO) framework is proposed for constrained aerodynamic shape optimization (ASO). The main contribution of the study is that ASO is promoted using historically aerodynamic process data generated during the gradient free optimization process. Meanwhile, nonparametric adjustment of the TLBO algorithm can help relieve manual design experience for actual engineering applications. Based on the structure of the TLBO algorithm, a model optimal prediction method is proposed as the new surrogate-assisted support strategy to accelerate the ASO process. The proposed method is applied to airfoil and wing shape designs to verify the optimization effect and efficiency. A benchmark aerodynamic design optimization is employed for the drag minimization of the RAE2822 airfoil. The optimized results indicate that the proposed method has advantages of high efficiency, strong optimization ability, and nonparametric characteristics for ASO. Moreover, the results of the wing shape optimization verify the advantages of the proposed methods over the surrogate-based optimization and direct optimization frameworks.
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Pairet, Eric, Constantinos Chamzas, Yvan R. Petillot, and Lydia Kavraki. "Path Planning for Manipulation Using Experience-Driven Random Trees." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 3295–302. http://dx.doi.org/10.1109/lra.2021.3063063.
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Endress, Felix, Jasper Rieser, and Markus Zimmermann. "ON THE TREATMENT OF REQUIREMENTS IN DFAM: THREE INDUSTRIAL USE CASES." Proceedings of the Design Society 3 (June 19, 2023): 2815–24. http://dx.doi.org/10.1017/pds.2023.282.
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AbstractOptimization-driven design offers advantages over traditional experience-based mechanical design. As an example, topology optimization can be a powerful tool to generate body shapes for Additive Manufacturing (AM). This is helpful, when (1) load paths are non-intuitive due to complex design domains or boundary conditions, or (2) the design process is to be automated to minimize effort associated with experience-based design. However, practically relevant boundary conditions are often difficult to put into a formal mathematical language to, for example, either feed it into a topology optimization algorithm, or provide precise quantitative criteria for CAE-supported manual design. This paper presents a survey of three industry use cases and identifies three types of requirements: the first can be directly cast into parts of an optimization problem statement (∼ 40%), the second is considered indirectly by adapting the optimization problem without explicit reference to the requirement (∼ 20%), and the third is only assessed after the design is finalized (∼ 40%). For categories 2 and 3 we propose directions of improvement to support formulating complex design tasks as unambiguous design problems.
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Borba Evangelista, Gustavo, Guilherme Conceição Rocha, and Wlamir Olivares Loesch Vianna. "Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1170.
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The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result.
In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.
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Na, Chongzheng, and Huixin Liu. "A Historical Experience Surrogate Model Assisted Particle Swarm Optimization for Expensive Black-box Problems." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 83–88. http://dx.doi.org/10.54097/hset.v7i.1021.
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This paper proposed a new historical experience surrogate model assisted particle swarm optimization method. This method extends the particle swarm optimization by adding new surrogate-based phase. In the classic phase, the particle swarm optimization runs the same way as the original algorithm, and the real function value evaluated are collected into the global database. In the surrogate phase, sub-swarms are generated following the distribution of the history data and evaluated by the surrogate model(s). The purpose of the surrogate phase is to explore the possible better solutions of the searching history. Also, the surrogate model(s) have the ability of accelerating the intelligence algorithms. Nevertheless, considering the time complexity of training and evaluating the surrogate model(s), the original problem should be expensive to evaluate or driven by data, which are same as many real-world problems.
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Enríquez-Urbano, Juana, Marco Antonio Cruz-Chávez, Rafael Rivera-López, Martín H. Cruz-Rosales, Yainier Labrada-Nueva, and Marta Lilia Eraña-Díaz. "Metaheuristic to Optimize Computational Convergence in Convection-Diffusion and Driven-Cavity Problems." Mathematics 9, no. 7 (March 31, 2021): 748. http://dx.doi.org/10.3390/math9070748.
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This work presents an optimization proposal to better the computational convergence time in convection-diffusion and driven-cavity problems by applying a simulated annealing (SA) metaheuristic, obtaining optimal values in relaxation factors (RF) that optimize the problem convergence during its numerical execution. These relaxation factors are tested in numerical models to accelerate their computational convergence in a shorter time. The experimental results show that the relaxation factors obtained by the SA algorithm improve the computational time of the problem convergence regardless of user experience in the initial low-quality RF proposal.
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Atri, Roozbeh, J. Marquez, Connie Leung, Masudur Siddiquee, Douglas Murphy, Ashraf Gorgey, William Lovegreen, Ding-Yu Fei, and Ou Bai. "Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography." Sensors 18, no. 8 (August 17, 2018): 2705. http://dx.doi.org/10.3390/s18082705.
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The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this model requires an exhaustive search over all the possible combinations of parameters, leading to computationally inefficient system. Various data-driven optimization methods were investigated and a modified Nelder–Mead algorithm using a Latin Hypercube Sampling method was introduced to tune the powered prosthetic. The results of the modified Nelder–Mead optimization were compared to the Exhaustive search, Genetic Algorithm, and conventional Nelder–Mead method, and the results showed the feasibility of using the presented method, to objectively calibrate the parameters in a time-efficient way using biological evidence.
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Chen, Dongdong, Linwei Wang, Xingjun Luo, Chunlong Fei, Di Li, Guangbao Shan, and Yintang Yang. "Recent Development and Perspectives of Optimization Design Methods for Piezoelectric Ultrasonic Transducers." Micromachines 12, no. 7 (June 30, 2021): 779. http://dx.doi.org/10.3390/mi12070779.
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A piezoelectric ultrasonic transducer (PUT) is widely used in nondestructive testing, medical imaging, and particle manipulation, etc., and the performance of the PUT determines its functional performance and effectiveness in these applications. The optimization design method of a PUT is very important for the fabrication of a high-performance PUT. In this paper, traditional and efficient optimization design methods for a PUT are presented. The traditional optimization design methods are mainly based on an analytical model, an equivalent circuit model, or a finite element model and the design parameters are adjusted by a trial-and-error method, which relies on the experience of experts and has a relatively low efficiency. Recently, by combining intelligent optimization algorithms, efficient optimization design methods for a PUT have been developed based on a traditional model or a data-driven model, which can effectively improve the design efficiency of a PUT and reduce its development cycle and cost. The advantages and disadvantages of the presented methods are compared and discussed. Finally, the optimization design methods for PUT are concluded, and their future perspectives are discussed.
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Kong, Xiangsong, Changqing Shi, Hang Liu, Pengcheng Geng, Jiabin Liu, and Yasen Fan. "Performance Optimization of a Steam Generator Level Control System via a Revised Simplex Search-Based Data-Driven Optimization Methodology." Processes 10, no. 2 (January 28, 2022): 264. http://dx.doi.org/10.3390/pr10020264.
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A Steam generator is a crucial device of a nuclear power plant. Control performance of the steam generator level control system is key to its normal operation. To improve its performance, the control system parameters should be optimized by utilizing a proper optimization method. Furthermore, the method’s efficiency is critical for its operability in the actual plant. However, the steam generator level process is a complex process, with high nonlinearity and time-varying properties. Traditional parameters tuning methods are experience-based, cumbersome, and time-consuming. To address the challenge, a systemic data-driven optimization methodology based on the model-free optimization with a revised simplex search method was proposed. Rather than the traditional controller parameter tuning method, this method optimizes the control system directly by using control performance measurements. To strengthen its efficiency, two critical modifications were incorporated into the traditional simplex search method to form a knowledge-informed simplex search based on historical gradient approximations. Firstly, with the help of the historical gradient approximations, the revised method could sense the optimization direction more accurately and accomplish the iteration step size tuning adaptively, significantly reducing the optimization cost. Secondly, a revised iteration termination control strategy was developed and integrated to monitor the optimization progress, which can promptly terminate the progress to avoid unnecessary iteration costs. The effectiveness and the efficiency of the revised method were demonstrated through simulation experiments.
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Shi, Zheyuan Ryan. "AI for Social Good: Between My Research and the Real World." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15732–33. http://dx.doi.org/10.1609/aaai.v35i18.17863.
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AI for social good (AI4SG) is a research theme that aims to use and advance AI to improve the well-being of society. My work on AI4SG builds a two-way bridge between the research world and the real world. Using my unique experience in food waste and security, I propose applied AI4SG research that directly addresses real-world challenges which have received little attention from the community. Drawing from my experience in various AI4SG application domains, I propose bandit data-driven optimization, the first iterative prediction-prescription framework and a no-regret algorithm PROOF. I will apply PROOF back to my applied work on AI4SG, thereby closing the loop in a single framework.
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Kang, Liuwang, Ankur Sarker, and Haiying Shen. "Velocity Optimization of Pure Electric Vehicles with Traffic Dynamics and Driving Safety Considerations." ACM Transactions on Internet of Things 2, no. 1 (February 2021): 1–24. http://dx.doi.org/10.1145/3433678.
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As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. Also, as velocity optimization is for individual vehicles, previous methods cannot avoid rear-end collisions. That is, a vehicle following its optimal velocity profile may experience rear-end collisions with its frontal vehicle on the road. In this article, for the first time, we propose a velocity optimization system that enables EVs to immediately pass green traffic lights without delay and to avoid rear-end collisions to ensure driving safety when EVs follow optimal velocity profiles on the road. We collected real driving data on road sections of US-25 highway (with two driving lanes in each direction and relatively low traffic volume) to conduct extensive trace-driven simulation studies. Results show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time. Also, it helps EVs to avoid possible collisions compared with existing collision avoidance methods.
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Tantawy, Alshaimaa A., Zenat Ahmed, and Mahmoud M. Ali. "Applying Big Data Analytics to Retail for Improved Supply Chain Visibility." American Journal of Business and Operations Research 4, no. 1 (2021): 39–46. http://dx.doi.org/10.54216/ajbor.040104.
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Retail supply chains generate huge volumes of data that can provide valuable insights if analyzed effectively. This paper explores how retailers can leverage Big Data analytics techniques on supply chain data to gain enhanced visibility into their operations. We examine three use cases of data-driven supply chain visibility: (1) predictive replenishment to anticipate future demand and optimize inventory levels; (2) personalized assortment optimization to tailor product selections for local customer segments; and (3) optimized order fulfillment to improve delivery times and reduce transportation costs. We analyze how retailers can apply machine learning algorithms and statistical analysis on point-of-sale data, inventory data, customer data and external data sources to uncover hidden patterns and drive data-driven decisions in these areas. The results include reduced excess inventory, fewer stock-outs, higher in-store product availability, lower fulfillment costs and improved customer experience. Data-driven supply chain visibility allows retailers to transition from a reactive, speculative business model to a predictive, personalized model that enhances competitiveness.
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Peng, Shuai, Jialu Hu, Han Xiao, Shujie Yang, and Changqiao Xu. "Viewport-Driven Adaptive 360◦ Live Streaming Optimization Framework." Journal of Networking and Network Applications 1, no. 4 (January 2022): 139–49. http://dx.doi.org/10.33969/j-nana.2021.010401.
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Virtual reality (VR) video streaming and 360◦ panoramic video have received extensive attention in recent years, which can bring users an immersive experience. However, the ultra-high bandwidth and ultra-low latency requirements of virtual reality video or 360◦ panoramic video also put tremendous pressure on the carrying capacity of the current network. In fact, since the user’s field of view (a.k.a viewport) is limited when watching a panoramic video and users can only watch about 20%∼30% of the video content, it is not necessary to directly transmit all high-resolution content to the user. Therefore, predicting the user’s future viewing viewport can be crucial for selective streaming and further bitrate decisions. Combined with the tile-based adaptive bitrate (ABR) algorithm for panoramic video, video content within the user’s viewport can be transmitted at a higher resolution, while areas outside the viewport can be transmitted at a lower resolution. This paper mainly proposes a viewport-driven adaptive 360◦ live streaming optimization framework, which combines viewport prediction and ABR algorithm to optimize the transmission of live 360◦ panoramic video. However, existing viewport prediction always suffers from low prediction accuracy and does not support real-time performance. With the advantage of convolutional network (CNN) in image processing and long short-term memory (LSTM) in temporal series processing, we propose an online-updated viewport prediction model called LiveCL which mainly utilizes CNN to extract the spatial characteristics of video frames and LSTM to learn the temporal characteristics of the user’s viewport trajectories. With the help of the viewport prediction and ABR algorithm, unnecessary bandwidth consumption can be effectively reduced. The main contributions of this work include: (1) a framework for 360◦ video transmission is proposed; (2) an online real-time viewport prediction model called LiveCL is proposed to optimize 360◦ video transmission combined with a novel ABR algorithm, which outperforms the existing model. Based on the public 360◦ video dataset, the tile accuracy, recall, precision, and frame accuracy of LiveCL are better than those of the latest model. Combined with related adaptive bitrate algorithms, the proposed viewport prediction model can reduce the transmission bandwidth by about 50%.
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Chotikunnan, Phichitphon, Rawiphon Chotikunnan, Anuchit Nirapai, Anantasak Wongkamhang, Pariwat Imura, and Manas Sangworasil. "Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques." Journal of Robotics and Control (JRC) 4, no. 2 (March 30, 2023): 128–40. http://dx.doi.org/10.18196/jrc.v4i2.18108.
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In this study, a method for optimizing membership function tuning for fuzzy control of robotic manipulators using PID-driven data techniques is presented. Traditional approaches for designing membership functions in fuzzy control systems often rely on the experience and knowledge of the system designer, which can lead to suboptimal performance. By utilizing data collected from a PID control system, the proposed method aims to enhance the precision and controllability of robotic manipulators through improved fuzzy logic control. A Mamdani-type fuzzy logic controller was developed and its performance was simulated in Simulink, demonstrating the effectiveness of the proposed optimization technique. The results indicate that the method can outperform conventional P control systems in terms of overshoot reduction while maintaining comparable transient response specifications. This research highlights the potential of the PID-driven data-based approach for optimizing membership function tuning in fuzzy control systems and offers valuable insights for the development and evaluation of fuzzy logic control in robotic manipulators. Future work may focus on further optimization of the tuning process, evaluation of system robustness under various operating conditions, and exploring the integration of other artificial intelligence techniques for improved control performance.
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Kupchak, Connor, Jerry Battista, and Jake Van Dyk. "Experience-driven dose-volume histogram maps of NTCP risk as an aid for radiation treatment plan selection and optimization." Medical Physics 35, no. 1 (December 26, 2007): 333–43. http://dx.doi.org/10.1118/1.2815943.
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Li, Jiajia. "Data-Driven Prediction of Students’ Online Learning Needs and Optimization of Knowledge Library Management." International Journal of Emerging Technologies in Learning (iJET) 18, no. 18 (September 25, 2023): 150–64. http://dx.doi.org/10.3991/ijet.v18i18.43503.
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Thanks to the advancement of information technology, online learning has become a crucial tool of modern education, and the management of modern education is facing the challenges of how to effectively predict students’ learning needs and how to optimize the management of the knowledge library to support these needs. However, existing data-driven prediction approaches are flawed in handling complex learning environments and timely adapting to changes, so this study attempts to solve these questions by exploring the correlation between learning needs, the extraction of knowledge linkages, and the optimization of knowledge libraries based on rule updates. In our work, a new method was proposed for extracting learning needs and knowledge linkages to more accurately identify and predict students’ learning needs, and a rule-based knowledge library management optimization method was introduced to allow the knowledge library to more flexibly adapt to students’ learning needs and the changes in educational resources. In this way, this study offers a comprehensive and flexible solution for education management via the combination of these two aspects, which not only increases student satisfaction and improves teaching quality but also reduces resource waste and gives students a more personalized and efficient learning experience. Furthermore, the methods and findings of this study could also be used as references for data-driven decision-making in other fields.
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Wang, Gaofeng, and Changhoon Shin. "Influencing Factors of Usage Intention of Metaverse Education Application Platform: Empirical Evidence Based on PPM and TAM Models." Sustainability 14, no. 24 (December 19, 2022): 17037. http://dx.doi.org/10.3390/su142417037.
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We explored the influencing factors of the usage intention of a metaverse education application platform that directly influence the optimization of its service function, improve the usage intention, and realize the promotion and application of metaverse technology in the education domain. Based on the characteristics of the metaverse education application platform, we integrated the PPM (push–pull–mooring) model and the TAM (technology acceptance model) to construct the model of influencing factors of usage intention. Ultimately, 275 valid questionnaires were collected through expert demonstration, pre-investigation, formal investigation, and other processes. In addition, our paper used the SEM (structural equation model) and fsQCA (fuzzy-set qualitative comparative analysis) to analyze the influencing factors of user willingness and their configuration paths. The study found that personalized learning, contextualized teaching, perceived usefulness, perceived ease of use, social needs, and social impact play significant positive roles in the willingness to use the metaverse education platform. Meanwhile, the obtained findings show that the experience-led community-driven mode, personality-led community-driven mode, and social-led utility-driven mode serve as potential guidelines for usage intention enhancement.
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Du, Zhuoming, Junfeng Zhang, and Bo Kang. "A Data-Driven Method for Arrival Sequencing and Scheduling Problem." Aerospace 10, no. 1 (January 7, 2023): 62. http://dx.doi.org/10.3390/aerospace10010062.
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Decision support tools for arrival sequencing and scheduling could assist air traffic controllers in managing the arrival aircraft in terminal areas. However, one critical issue is that the current method for dealing with the arrival sequencing and scheduling problem does not consider the dynamic traffic situation and the human working experience, which results in a deviation between the scheduled and actual landing sequences. This paper develops a data-driven method to address this issue. Firstly, the random forest model is applied to predict the estimated time of arrival (ETA). During the ETA prediction, the trajectory, operation, and airport-related factors that could increase the prediction accuracy are considered. Secondly, the landing sequence is obtained by sorting the predicted ETAs. Thirdly, two optimization methods are proposed to generate the scheduled time of arrival (STA). The former uses the predicted ETAs as inputs and then directly optimizes the landing sequence and the STA. The latter uses both the predicted ETA and the landing sequence as inputs for further optimization. Finally, these proposed methods are evaluated with three sets of historical data on arrival operations at Changsha Huanghua International Airport (ZGHA). The results show that the RF-based ETA prediction method could improve scheduling performance. Moreover, the proposed optimization methods could provide controllers with a more appropriate decision advisory. Such advisories could simultaneously reduce the operation efficiency indicators (average/maximum delay or dwell time) and the operation complexity indicators (Kendall rank correlation or position shift).
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Ming, Zhao, Xiuhua Li, Chuan Sun, Qilin Fan, Xiaofei Wang, and Victor C. M. Leung. "Sleeping Cell Detection for Resiliency Enhancements in 5G/B5G Mobile Edge-Cloud Computing Networks." ACM Transactions on Sensor Networks 18, no. 3 (August 31, 2022): 1–30. http://dx.doi.org/10.1145/3512893.
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The rapid increase of data traffic has brought great challenges to the maintenance and optimization of 5G and beyond, and some smart critical infrastructures, e.g., small base stations (SBSs) in cellular cells, are facing serious security and failure threats, causing resiliency degradation concerns. Among special smart critical infrastructure failures, the sleeping cell failure is hard to address since no alarm is generally triggered. Sleeping cells can remain undetected for a long time and can severely affect the quality of service/quality of experience to users. To enhance the resiliency of the SBSs in sleeping cells, we design a mobile edge-cloud computing system and propose a semi-supervised learning-based framework to dynamically detect the sleeping cells. Particularly, we consider two indicators, recovery proportion and recovery speed, to measure the resiliency of the SBSs. Moreover, in the proposed scheme, experts’ optimization experience and each period’s detection results can be utilized to iteratively improve the performance. Then we adopt a dataset from real-world networks for performance evaluation. Trace-driven evaluation results demonstrate that the proposed scheme outperforms existing sleeping cell detection schemes, and can also reduce the communication and runtime costs and enhance the resiliency of the SBSs.
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Bertoni, Alessandro. "MITIGATING UNCERTAINTY IN CONCEPTUAL DESIGN USING OPERATIONAL SCENARIO SIMULATIONS: A DATA-DRIVEN EXTENSION OF THE EVOKE APPROACH." Proceedings of the Design Society 3 (June 19, 2023): 2665–74. http://dx.doi.org/10.1017/pds.2023.267.
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AbstractThe paper presents an approach where the iterative replication of Discrete Event Simulations on future operational scenarios is used to derive data-driven design merit functions. The presented contribution proposes an extension of the EVOKE (Early Value Oriented Design Exploration with Knowledge Maturity) approach determining when and how the experience-based judgment about maximization, minimization, optimization, and avoidance functions, correlating value drivers and quantified objectives, can be substituted by data-driven mathematical functions obtained by scenarios simulations. The approach is described through a simplified case concerning the development of autonomous electric vehicles to complement the public transport system in the city of Karlskrona in Sweden. The consideration of value drivers and quantified objectives presented is meant to support a preliminary screening of potential design configurations to support the definition of high-level product and system-related functional requirements, to be run before a more detailed conceptual design analysis.
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Ye, Lixiao, Nan Zhang, Guanghui Li, Dungang Gu, Jiaqi Lu, and Yuhang Lou. "Intelligent Optimization Design of Distillation Columns Using Surrogate Models Based on GA-BP." Processes 11, no. 8 (August 8, 2023): 2386. http://dx.doi.org/10.3390/pr11082386.
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The design of distillation columns significantly impacts the economy, energy consumption, and environment of chemical processes. However, optimizing the design of distillation columns is a very challenging problem. In order to develop an intelligent technique to obtain the best design solution, improve design efficiency, and minimize reliance on experience in the design process, a design methodology based on the GA-BP model is proposed in this paper. Firstly, a distillation column surrogate model is established using the back propagation neural network technique based on the training data from the rigorous simulation, which covers all possible changes in feed conditions, operating conditions, and design parameters. The essence of this step is to turn the distillation design process from model-driven to data-driven. Secondly, the model takes the minimum TAC as the objective function and performs the optimization search using a Genetic Algorithm to obtain the design solution with the minimum TAC, in which a life-cycle assessment (LCA) model is incorporated to evaluate the obtained optimized design solution from both economic and environmental aspects. Finally, the feasibility of the proposed method is verified with a propylene distillation column as an example. The results show that the method has advantages in convergence speed without sacrificing accuracy and can obtain an improved design solution with reduced cost and environmental impact. Compared with the original design using rigorous simulation, the TAC is reduced by 6.1% and carbon emission by 27.13 kgCO2/t.
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Xu, Chen, Decun Dong, Dongxiu Ou, and Changxi Ma. "Time-of-day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections." International Journal of Environmental Research and Public Health 16, no. 5 (March 9, 2019): 870. http://dx.doi.org/10.3390/ijerph16050870.
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This paper proposes a novel two-order optimization model of the division of time-of-day control segmented points of road intersection to address the limitations of the randomness of artificial experience, avoid the complex multi-factor division calculation, and optimize the traditional model over traffic safety and data-driven methods. For the first-order optimization—that is, deep optimization of the model input data—we first increase the dimension of traditional traffic flow data by data-driven and traffic safety methods, and develop a vector quantity to represent the size, direction, and time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. Then, a time-series segmentation algorithm is used to recurse the distance amongst adjacent vectors to obtain the initial scheme of segmented points, and the segmentation points are finally divided by the combination of the preliminary scheme. For the second-order optimization—that is, model adaptability analysis—the traffic flow data at intersections are subjected to standardised processing by five-number summary. The different traffic flow characteristics of the intersection are categorised by the K central point clustering algorithm of big data, and an applicability analysis of each type of intersection is conducted by using an innovated piecewise point division model. The actual traffic flow data of 155 intersections in Yuecheng District, Shaoxing, China, in 2016 are tested. Four types of intersections in the tested range are evaluated separately by the innovated piecewise point division model and the traditional total flow segmentation model on the basis of Synchro 7 simulation software. It is shown that when the innovated double-order optimization model is used in the intersection according to the ‘hump-type’ traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 5.6%. In particular, the delay time in the near-peak-flow buffer period is significantly reduced by approximately 17%. At the same time, the traffic accident rate has also dropped significantly, effectively improving traffic safety at intersections.
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Aldrich, Susan E. "Recommender Systems in Commercial Use." AI Magazine 32, no. 3 (June 9, 2011): 28–34. http://dx.doi.org/10.1609/aimag.v32i3.2368.
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Commercial recommender systems are deployed by marketing teams to increase revenue and/or personalize user experience. Marketers evaluate recommender systems not on its algorithms but on how well the vendor‘s expertise and interfaces will support achieving business goals. Driven by a business model that pays based on recommendation success, vendors guide clients through continuous optimization of recommendations. While recommender technology is mature, the solutions and market are still young. As a result, solutions are not fully integrated with other business systems and technology platforms. While the market is retail-focused today, interest and vendor offerings are rapidly expanding to other areas. Retail clients will drive social, location, and mobile enhancements.
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Pimentel-Niño, M. A., Paresh Saxena, and M. A. Vazquez-Castro. "Reliable Adaptive Video Streaming Driven by Perceptual Semantics for Situational Awareness." Scientific World Journal 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/394956.
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A novel cross-layer optimized video adaptation driven by perceptual semantics is presented. The design target is streamed live video to enhance situational awareness in challenging communications conditions. Conventional solutions for recreational applications are inadequate and novel quality of experience (QoE) framework is proposed which allows fully controlled adaptation and enables perceptual semantic feedback. The framework relies on temporal/spatial abstraction for video applications serving beyond recreational purposes. An underlying cross-layer optimization technique takes into account feedback on network congestion (time) and erasures (space) to best distribute available (scarce) bandwidth. Systematic random linear network coding (SRNC) adds reliability while preserving perceptual semantics. Objective metrics of the perceptual features in QoE show homogeneous high performance when using the proposed scheme. Finally, the proposed scheme is in line with content-aware trends, by complying with information-centric-networking philosophy and architecture.
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Bekasiewicz, Adrian, Slawomir Koziel, Piotr Plotka, and Krzysztof Zwolski. "EM-Driven Multi-Objective Optimization of a Generic Monopole Antenna by Means of a Nested Trust-Region Algorithm." Applied Sciences 11, no. 9 (April 27, 2021): 3958. http://dx.doi.org/10.3390/app11093958.
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Antenna structures for modern applications are characterized by complex and unintuitive topologies that are difficult to develop when conventional, experience-driven techniques are of use. In this work, a method for the automatic generation of antenna geometries in a multi-objective setup has been proposed. The approach involves optimization of a generic spline-based radiator with an adjustable number of parameters using a nested, trust region-based algorithm. The latter iteratively increases the dimensionality of the radiator in order to gradually improve its performance. The method has been used to generate a set of nine antenna designs, representing a trade-off between minimization of reflection within 3.1 GHz to 10.6 GHz and a reduction of size. The properties of the optimized designs vary along the Pareto set from −10 dB to −20 dB and from 230 mm2 to 757 mm2 for the first and second objectives, respectively. The presented design approach has been validated against a genuine, population-based optimization routine. Furthermore, the smallest Pareto-optimal design has been compared to the antennas from the literature.
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Lehmann, Daniel, Diego Hidalgo Rodriguez, and Michel Brack. "Optimized operation of large scale battery systems." at - Automatisierungstechnik 70, no. 1 (January 1, 2022): 67–78. http://dx.doi.org/10.1515/auto-2021-0114.
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Abstract In the decentralized renewable driven electric energy system, economically viable battery systems become increasingly important for providing grid-related services. End of 2016, STEAG has successfully started the commercial operation of six 15 MW large scale battery systems which have been incorporated in STEAG’s primary control pool. During the commissioning phase, extensive effort has been spent in optimizing the operational efficiency of these systems with promising results. However, the operation experience has shown that there is still significant potential for improving the system behavior as well as reducing the aging of the battery cells. By analyzing historical data of the charging power associated with the state of charge management, opportunities for significantly reducing the operational costs have been identified. By means of more involved model-based control strategies, which adequately consider the specific characteristics of the battery system, and by using mathematical optimization and artificial intelligence, adapting the state of charge management strategy to new applications, these additional cost savings can be obtained. Apart from giving insights into the operational experience with large scale battery systems, the contribution of this paper lies in proposing strategies for reducing the operational costs of the battery system using classical approaches as well as mathematical optimization and neural networks. These approaches will be illustrated by simulation results.
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Li, Bin, Xifan Yao, Yongxiang Li, Wei Tan, Huidong Lou, and Dongyuan Ge. "Simulation & Optimization of the Gear System of a 6-DOF Manipulator Using Flexible Dynamic of ANSYS." Open Mechanical Engineering Journal 8, no. 1 (March 21, 2014): 69–76. http://dx.doi.org/10.2174/1874155x01408010069.
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This paper presents a novel way for a structural dynamic simulation analysis on a three-dimensional (3-D) finite element (FE) model of a 6-DOF Manipulator using ANSYS Workbench 13.0 that allows integrated optimization. The load between driving and driven gear is delivered by elastic frictional contact, which leads to some non-liner contact problems, and the contact problems are solved according to the FE parametric programming method. This study particularly focused on investigating static, dynamic, and fatigue behaviors of the gear system in a 6-DOF robot mechanism, which is modeled using SolidWorks software. Moreover, the ANSYS Workbench was used to determine the stress distribution, deformation and fatigue behaviors of the mechanism, and finally to carry out the optimization simulation analysis of its materials together with structural geometry. As a result, the design experience accumulated will be very useful for the future product design in terms of guidelines for even more complex mechanical systems or more complex boundary conditions.
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Li, Zhengyuan, Jie Chen, Yanmei Meng, Jihong Zhu, Jiqin Li, Yue Zhang, and Chengfeng Li. "Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model." Foods 11, no. 23 (November 28, 2022): 3845. http://dx.doi.org/10.3390/foods11233845.
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The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers’ experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF–EF–IF) by introducing synergetic theory, and each flow’s order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters—including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction—are produced as outputs. A collaborative optimization model of the MF–EF–IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.
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Liu, Xingdong, Daolin Xu, Hui Peng, XiaoChuan Xu, HuiDeng Liu, and Xin Zhang. "Research on Data-Driven Distribution Network Planning Method." Scientific Programming 2022 (August 16, 2022): 1–7. http://dx.doi.org/10.1155/2022/7838269.
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With the development of the intelligent and interactive power system, the elements of distribution network planning continue to increase. The distribution network connects the transmission system and individuals, directly affects the individual’s power consumption experience, and is a key link in the power system. A reasonable planning scheme can not only improve the power supply capacity and reliability of the distribution network but also fully apply the data of each system in the distribution network to realize the optimal planning of the medium-voltage distribution network driven by data. Firstly, this paper constructs the CIM model and the distribution network topology model and establishes the wiring pattern recognition feature library. The network reconstruction and planning method research was carried out for the target line, and a typical operation scenario of the distribution network was generated. At the same time, based on the time period network loss index, the distribution network reconfiguration optimization model and distribution network expansion planning model are established, and the solution method of the distribution network reconstruction and expansion planning model is expounded. A reconstruction optimization scheme with the best overall network loss performance is in the network operation scenario. The experimental results of the final example show that based on the proposed time period network loss index, the overall operation loss of the distribution network in a period can be calculated more accurately, and the optimized planning scheme is more suitable for the load power consumption characteristics of the region, and the method has certain feasibility.
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Tang, Wenyun, Le Xu, and Jianxiao Ma. "Modeling Autonomous Vehicles’ Altruistic Behavior to Human-Driven Vehicles in the Car following Events and Impact Analysis." Journal of Advanced Transportation 2023 (April 10, 2023): 1–14. http://dx.doi.org/10.1155/2023/4060451.
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To explore the impact of autonomous vehicles (AVs) on human-driven vehicles (HDVs), a solution for AV to coexist harmoniously with HDV during the car following period when AVs are in low market penetration rate (MPR) was provided. An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. The pretreated Next Generation Simulation (NGSIM) dataset was used for the experiments. 1027 car following events with being redefined were extracted from it, in which 600 of the events were used for training and 427 of the events were used for testing. The different driving strategies obtained from the classical car following models were embedded into virtual environment built by OpenAI gym. The reward function combined safety, efficiency, jerk, and stability was used to encourage the agent with DDPG algorithm to maximize it. The final result reveals that disturbance of HDV followers decreases by 2.362% (strategy a), 8.184% (strategy b), and 13.904% (strategy c), respectively. The disturbance of HDV follower decreases by 14.961% (strategy a), 12.020% (strategy b), and 13.425% (strategy c), respectively. HDV followers with different strategies get less jerk in both soft optimizations. AV passengers get a loss on jerk and efficiency, but safety is enhanced. Also, AV car following performs better than HDV car following in both soft and brutal optimizations. Moreover, two possible solutions for harmonious coexistence of HDVs and AVs when AVs are in low MPR are proposed.
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Wang, Yimeng, Mridul Agarwal, Tian Lan, and Vaneet Aggarwal. "Learning-Based Online QoE Optimization in Multi-Agent Video Streaming." Algorithms 15, no. 7 (June 28, 2022): 227. http://dx.doi.org/10.3390/a15070227.
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Video streaming has become a major usage scenario for the Internet. The growing popularity of new applications, such as 4K and 360-degree videos, mandates that network resources must be carefully apportioned among different users in order to achieve the optimal Quality of Experience (QoE) and fairness objectives. This results in a challenging online optimization problem, as networks grow increasingly complex and the relevant QoE objectives are often nonlinear functions. Recently, data-driven approaches, deep Reinforcement Learning (RL) in particular, have been successfully applied to network optimization problems by modeling them as Markov decision processes. However, existing RL algorithms involving multiple agents fail to address nonlinear objective functions on different agents’ rewards. To this end, we leverage MAPG-finite, a policy gradient algorithm designed for multi-agent learning problems with nonlinear objectives. It allows us to optimize bandwidth distributions among multiple agents and to maximize QoE and fairness objectives on video streaming rewards. Implementing the proposed algorithm, we compare the MAPG-finite strategy with a number of baselines, including static, adaptive, and single-agent learning policies. The numerical results show that MAPG-finite significantly outperforms the baseline strategies with respect to different objective functions and in various settings, including both constant and adaptive bitrate videos. Specifically, our MAPG-finite algorithm maximizes QoE by 15.27% and maximizes fairness by 22.47% compared to the standard SARSA algorithm for a 2000 KB/s link.
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Yu, Ran, Hongsheng Cheng, Yun Ye, Qin Wang, Shuping Fan, Tan Li, Cheng Wang, Yue Su, and Xingyu Zhang. "Optimization of the Territorial Spatial Patterns Based on MOP and PLUS Models: A Case Study from Hefei City, China." International Journal of Environmental Research and Public Health 20, no. 3 (January 18, 2023): 1804. http://dx.doi.org/10.3390/ijerph20031804.
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Optimization of the territorial spatial patterns can promote the functional balance and utilization efficiency of space, which is influenced by economic, social, ecological, and environmental factors. Consequently, the final implementation of spatial planning should address the issue of sustainable optimization of territorial spatial patterns, driven by multiple objectives. It has two components—the territorial spatial scale prediction and its layout simulation. Because a one-sided study of scale or layout is divisive, it is necessary to combine the two to form complete territorial spatial patterns. This paper took Hefei city as an example and optimized its territorial spatial scale using the multiple objective programming (MOP) model, with four objective functions. A computer simulation of the territorial spatial layout was created, using the patch-generating land use simulation (PLUS) model, with spatial driving factors, conversion rules, and the scale optimization result. To do this, statistical, empirical, land utilization, and spatially driven data were used. The function results showed that carbon accumulation and economic and ecological benefits would be ever-increasing, and carbon emissions would reach their peak in 2030. The year 2030 was a vital node for the two most important land use types in the spatial scale—construction land and farmland. It was projected that construction land would commence its transition from reduced to negative growth after that time, and farmland would start to rebound. The simulation results indicated that construction land in the main urban area would expand primarily to the west, with supplemental expansion to the east and north. In contrast, construction land in the counties would experience a nominal increase, and a future ecological corridor would develop along the route south of Chaohu County–Chaohu Waters–Lujiang County–south of Feixi County.
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Kaliuta, K. "Personalizing the user experience in Salesforce using AI technologies." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 52 (September 24, 2023): 48–53. http://dx.doi.org/10.36910/6775-2524-0560-2023-52-06.
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Artificial intelligence (AI) technology has transformed the interactive marketing experience of clients. Despite a large number of studies investigating the use of AI in interactive marketing, customization as a significant concept remains unexplored in marketing using AI research and practice. authors investigate appropriate research as well as insights on Salesforce interactive advertising. The authors highlight practice challenges at various points of the customer experience and give critical managerial advice as a solution to such problems. Personalization may help your business in a variety of ways. For starters, it may enhance customer loyalty and engagement by making consumers feel appreciated and understood. Second, it may aid your company's revenue growth by encouraging clients to make more purchases based on personalized suggestions. Third, it can boost retention rates of clients by increasing trust and decreasing the possibility that consumers would migrate to a rival. Fourth, it may enhance overall consumer satisfaction through rendering it more easy, efficient, and pleasurable. Your company is well-positioned to provide uniquely customised experience to customers at scale by embracing AI-powered customization technology. AI can aid in the discovery of important consumer data, the generation of forecasts about consumer habits and preferences, and the optimization of content and product suggestions. One can guarantee that AI-driven personalisation is successful and satisfies your customers' expectations by doing so.
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Naddeo, Alessandro, and Nicola Cappetti. "Comfort driven design of innovative products: A personalized mattress case study." Work 68, s1 (January 8, 2021): S139—S150. http://dx.doi.org/10.3233/wor-208013.
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BACKGROUND: Human-centred design asks for wellbeing and comfort of the customer/worker when interacting with a product. Having a good perception-model and an objective method to evaluate the experienced (dis)comfort by the product user is needed for performing a preventive comfort evaluation as early as possible in the product development plan. The mattress of a bed is a typical product whose relevance in everyday life of people is under-evaluated. Fortunately, this behaviour is quickly changing, and the customer wants to understand the product he/she buys and asks for more comfortable and for scientifically assessed products. No guidelines for designing a personalized mattress are available in the literature. OBJECTIVES: This study deals with the experience of designing an innovative product whose product-development-plan is focused on the customer perceived comfort: a personalized mattress. The research question is: which method can be used to innovate or create a comfort-driven human-centred product? METHODS: Virtual prototyping was used to develop a correlated numerical model of the mattress. A comfort model for preventively assessing the perceived comfort was proposed and experimentally tested. Mattress testing sessions with subjects were organized, and collected data were compared with already tested mattresses. Brainstorming and multi-expert methods were used to propose, realize, and test an archetype of a new mattress for final comfort assessment. RESULTS: A new reconfigurable mattress was developed, resulting in two patents. The mattress design shows that personalized products can be tuned according to the anthropometric data of the customer in order to improve the comfort experience during sleep. CONCLUSIONS: A “comfort-driven design guideline” was proposed; this method has been based on the use of virtual prototyping, virtual optimization and physical prototyping and testing. It allowed to improve an existing product in a better way and to bring innovation in it.
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Alizard, F., A. Cadiou, L. Le Penven, B. Di Pierro, and M. Buffat. "Space–time dynamics of optimal wavepackets for streaks in a channel entrance flow." Journal of Fluid Mechanics 844 (April 6, 2018): 669–706. http://dx.doi.org/10.1017/jfm.2018.191.
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The laminar–turbulent transition of a plane channel entrance flow is revisited using global linear optimization analyses and direct numerical simulations. The investigated case corresponds to uniform upstream velocity conditions and a moderate value of Reynolds number so that the two-dimensional developing flow is linearly stable under the parallel flow assumption. However, the boundary layers in the entry zone are capable of supporting the development of streaks, which may experience secondary instability and evolve to turbulence. In this study, global optimal linear perturbations are computed and studied in the nonlinear regime for different values of streak amplitude and optimization time. These optimal perturbations take the form of wavepackets having either varicose or sinuous symmetry. It is shown that, for short optimization times, varicose wavepackets grow through a combination of Orr and lift-up effects, whereas for longer target times, both sinuous and varicose wavepackets exhibit an instability mechanism driven by the presence of inflection points in the streaky flow. In addition, while the optimal varicose modes obtained for short optimization times are localized near the inlet, where the base flow is strongly three-dimensional, when the target time is increased, the sinuous and varicose optimal modes are displaced farther downstream, in the nearly parallel streaky flow. Finally, the optimal wavepackets are found to lead to turbulence for sufficiently high initial amplitudes. It is noticed that the resulting turbulent flows have the same wall-shear stress, whether the wavepackets have been obtained for short or for long time optimization.
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Wei, Hechuan, Boyuan Xia, Zhiwei Yang, and Zhexuan Zhou. "Model and Data-Driven System Portfolio Selection Based on Value and Risk." Applied Sciences 9, no. 8 (April 22, 2019): 1657. http://dx.doi.org/10.3390/app9081657.
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System portfolio selection is a kind of tradeoff analysis and decision-making on multiple systems as a whole to fulfill the overall performance on the perspective of System of Systems (SoS). To avoid the subjectivity of traditional expert experience-dependent models, a model and data-driven approach is proposed to make an advance on the system portfolio selection. Two criteria of value and risk are used to indicate the quality of system portfolios. A capability gap model is employed to determine the value of system portfolios, with the weight information determined by correlation analysis. Then, the risk is represented by the remaining useful life (RUL), which is predicted by analyzing time series of system operational data. Next, based on the value and risk, an optimization model is proposed. Finally, a case with 100 candidate systems is studied under the scenario of anti-missile. By utilizing the Non-dominated Sorting Differential Evolution (NSDE) algorithm, a Pareto set with 200 individuals is obtained. Some characters of the Pareto set are analyzed by discussing the frequency of being selected and the association rules. Through the conclusion of the whole procedures, it can be proved that the proposed model and data-driven approach is feasible and effective for system portfolio selection.
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Liu, Yuting, Wenchong Tian, Jun Xie, Weizhong Huang, and Kunlun Xin. "LSTM-Based Model-Predictive Control with Rationality Verification for Bioreactors in Wastewater Treatment." Water 15, no. 9 (May 5, 2023): 1779. http://dx.doi.org/10.3390/w15091779.
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With the increasing demands for higher treatment efficiency, better effluent quality, and energy conservation in Urban Wastewater Treatment Plants (WWTPs), research has already been conducted to construct an optimized control system for Anaerobic-Anoxic-Oxic (AAO) process using a data-driven approach. However, existing data-driven optimization control systems for AAO mainly focus on improving effluent water quality and reducing energy consumption, therefore they lack consideration for the stability of bioreactors. Meanwhile, safety in the optimization control process is still missing, resulting in a lack of reliability in practical applications. In this study, long short-term memory based model-predictive control (LSTM-MPC) with safety verificationis developed for the real-time control of AAO. It is used to optimize the control of aeration volume, internal recirculation, and sludge internal recycle processes for both saving energy and maintaining the stability of the bioreactor operation. To ensure the safety of the control process, this study proposes three rationality verification methods based on historical operation experience. These methods are validated through data from a real-world WWTP in eastern China. The results show that the prediction model of LSTM-MPC is capable of accurately predicting the water quality variables of the AAO system, with mean square error (MSE) close to 2.64 and Nash–Sutcliffe model efficiency coefficient (NSE) of 0.99 on the validation dataset. The combination of LSTM-MPC and rationality verification achieves a stable control trajectory with a 7% reduction in oxygen usage compared to a conventional controller, demonstrating its efficacy as a safe and reliable control strategy for WWTPs.
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Burns, Geoffrey T., Kenneth M. Kozloff, and Ronald F. Zernicke. "Biomechanics of Elite Performers: Economy and Efficiency of Movement." Kinesiology Review 9, no. 1 (February 1, 2020): 21–30. http://dx.doi.org/10.1123/kr.2019-0058.
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Movement is essential to the human experience, and efficient biomechanics facilitate effective action across the breadth of tasks one encounters in life. The concept of movement efficiency has been investigated and explored through a variety of means including biomechanical modeling, simulation, and experimental manipulation. Observations of elite performers for a given movement task serve as an additional line of insight into efficiency, as their movements have been driven toward optimization via competitive pressure. The authors first discuss the concept of efficiency in biomechanics from a qualitative perspective and the broad tools with which we explore it. They then highlight biomechanical investigations of elite performers and their contributions to our understanding of efficiency. Examples from various classes of movements illustrate unique insights of the elite performers in informing our understanding of movement efficiency.
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Brain, Jennifer. "Visual Representation of Safety Cases." Measurement and Control 45, no. 3 (April 2012): 82–86. http://dx.doi.org/10.1177/002029401204500303.
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Historically, safety cases in the nuclear industry have tended to follow a visually similar approach; descriptive text using a (gradually evolving) template driven in part by a desire to minimise the scope of changes to legacy safety cases. However, a number of recent factors are beginning to challenge this approach. Academic work and developments in other industries are proposing new structured and diagrammatic techniques and tools such as Claims-Arguments-Evidence and Goal Structured Notation. Additionally, new build projects are offering a ‘clean-sheet’ and an opportunity to break away from a traditional safety case presentation. This paper introduces a number of techniques for representing a safety case and identifies some of the considerations for selecting a presentation. It then presents the Claims-Arguments-Evidence approach proposed to support the C&I safety case for new build in order to describe some real life experience.
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Liu, Sixin, Yuhan Chen, Chaopeng Luo, Hejun Jiang, Hong Li, Hongqing Li, and Qi Lu. "Particle Swarm Optimization-Based Variational Mode Decomposition for Ground Penetrating Radar Data Denoising." Remote Sensing 14, no. 13 (June 22, 2022): 2973. http://dx.doi.org/10.3390/rs14132973.
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Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD algorithm determines the central frequency and bandwidth of each Intrinsic Mode Function (IMF) by iteratively searching for the optimal solution of the variational mode and is capable of adaptively and effectively dividing the signal in the frequency domain into the many IMFs. However, the penalty parameter α and the number of IMFs K in VMD processing are determined depending on manual experience, which are important parameters affecting the decomposition results. In this paper, we propose a method to automatically search the parameters α and K optimally by Particle Swarm Optimization (PSO) algorithm. Then the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are used to judge the best superposition of the IMFs for data reconstruction, and the process is data-driven without human subjective intervention. The proposed method is used to process the field data, and the reconstruction data show that this innovative VMD processing can effectively improve the SNR and highlight the target reflections, even some targets not found in pre-processing are also revealed.
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Liu, Risheng, Yuxi Zhang, Shichao Cheng, Xin Fan, and Zhongxuan Luo. "A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4368–75. http://dx.doi.org/10.1609/aaai.v33i01.33014368.
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Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indications. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-theart techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene.
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Cheung, Chi Fai, Wing Bun Lee, Sandy To, H. F. Li, and Su Juan Wang. "A Framework of a Model-Based Simulation System for Prediction of Surface Generation in Fast Tool Servo Machining of Optical Microstructures." Key Engineering Materials 339 (May 2007): 407–11. http://dx.doi.org/10.4028/www.scientific.net/kem.339.407.
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The fabrication of high-quality optical microstructural surfaces is based on fast tool servo (FTS) machining. It makes use of auxiliary piezo-electric driven servos to rapidly actuate the diamond tool with a fine resolution and a sufficiently high bandwidth for machining optical microstructures with submicrometer form accuracy and a nanometric surface finish without the need for any subsequent post processing. However, the achievement of a superior mirror finish and form accuracy still depends largely on the experience and skills of the machine operators, acquired through an expensive trial-and-error approach to using new materials, new mircostructural surface designs, or new machine tools. As a result, this paper, a model-based simulation system is presented for the optimization of surface quality in the FTS machining of optical microstructures. Preliminary experimental work and the results are also presented.
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VASYLKIVSKYI, Mikola, Ganna VARGATYUK, and Olga BOLDYREVA. "INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (February 23, 2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.
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The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key components of the intelligent controller in the future. The joint optimization of these components, which will provide better system performance, is considered. It was determined that instead of using a complex mathematical method of optimization, it is possible to use machine learning, which has less complexity and can adapt to network conditions. A 6G radio interface design based on a combination of model-driven and data-driven artificial intelligence is investigated and is expected to provide customized radio interface optimization from pre-configuration to self-learning. The specifics of configuring the network scheme and transmission parameters at the level of subscriber equipment and services using a personalized radio interface to maximize the individual user experience without compromising the throughput of the system as a whole are determined. Artificial intelligence is considered, which will be a built-in function of the radio interface that creates an intelligent physical layer and is responsible for MAC access control, network management optimization (such as load balancing and power saving), replacing some non-linear or non-convex algorithms in receiver modules or compensation of shortcomings in non-linear models. Built-in intelligence has been studied, which will make the 6G physical layer more advanced and efficient, facilitate the optimization of structural elements of the physical layer and procedural design, including the possible change of the receiver architecture, will help implement new detection and positioning capabilities, which, in turn, will significantly affect the design of radio interface components. The requirements for the 6G network are defined, which provide for the creation of a single network with scanning and communication functions, which must be integrated into a single structure at the stage of radio interface design. The specifics of carefully designing a communication and scanning network that will offer full scanning capabilities and more fully meet all key performance indicators in the communications industry are explored.
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Li, Yan. "Federated Deep Reinforcement Learning-Based Caching and Bitrate Adaptation for VR Panoramic Video in Clustered MEC Networks." Electronics 11, no. 23 (November 30, 2022): 3968. http://dx.doi.org/10.3390/electronics11233968.
Full textAbstract:
Virtual reality (VR) panoramic video is more expressive and experiential than traditional video. With the accelerated deployment of 5G networks, VR panoramic video has experienced explosive development. The large data volume and multi-viewport characteristics of VR panoramic videos make it more difficult to cache and transcode them in advance. Therefore, VR panoramic video services urgently need to provide powerful caching and computing power over the edge network. To address this problem, this paper establishes a hierarchical clustered mobile edge computing (MEC) network and develops a data perception-driven clustered-edge transmission model to meet the edge computing and caching capabilities required for VR panoramic video services. The joint optimization problem of caching and bitrate adaptation can be formulated as a Markov Decision Process (MDP). The federated deep reinforcement learning (FDRL) algorithm is proposed to solve the problem of caching and bitrate adaptation (called FDRL-CBA) for VR panoramic video services. The simulation results show that FDRL-CBA can outperform existing DRL-based methods in the same scenarios in terms of cache hit rate and quality of experience (QoE). In conclusion, this work developed a FDRL-CBA algorithm based on a data perception-driven clustered-edge transmission model, called Hierarchical Clustered MEC Networks. The proposed method can improve the performance of VR panoramic video services.
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Shahbazi, Zeinab, and Slawomir Nowaczyk. "Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization." Smart Cities 6, no. 5 (September 27, 2023): 2574–92. http://dx.doi.org/10.3390/smartcities6050116.
Full textAbstract:
In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.
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Drivas, Ioannis C., Damianos P. Sakas, Georgios A. Giannakopoulos, and Daphne Kyriaki-Manessi. "Big Data Analytics for Search Engine Optimization." Big Data and Cognitive Computing 4, no. 2 (April 2, 2020): 5. http://dx.doi.org/10.3390/bdcc4020005.
Full textAbstract:
In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.