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Liyakathunisa, Abdullah Alsaeedi, Saima Jabeen, and Hoshang Kolivand. "Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques." Journal of Ambient Intelligence and Smart Environments 14, no. 1 (January 20, 2022): 5–23. http://dx.doi.org/10.3233/ais-210162.
Повний текст джерелаАнотація:
Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.
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Servant, Mathieu, Gabriel Tillman, Jeffrey D. Schall, Gordon D. Logan, and Thomas J. Palmeri. "Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence." Journal of Neurophysiology 121, no. 4 (April 1, 2019): 1300–1314. http://dx.doi.org/10.1152/jn.00507.2018.
Повний текст джерелаАнотація:
Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
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Meng, Zhaorui, and Xianze Xu. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment." Energies 12, no. 24 (December 4, 2019): 4612. http://dx.doi.org/10.3390/en12244612.
Повний текст джерелаАнотація:
Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).
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Wali, S., M. H. U. Haq, M. Kazmi, and S. A. Qazi. "An End-to-End Machine Learning based Unified Architecture for Non-Intrusive Load Monitoring." Engineering, Technology & Applied Science Research 11, no. 3 (June 10, 2021): 7217–22. http://dx.doi.org/10.48084/etasr.4142.
Повний текст джерелаАнотація:
Non-Intrusive Load Monitoring (NILM) or load disaggregation aims to analyze power consumption by decomposing the energy measured at the aggregate level into constituent appliances level. The conventional load disaggregation framework consists of signal processing and machine learning-based pipelined architectures, respectively for explicit feature extraction and decision making. Manual feature selection in such load disaggregation frameworks leads to biased decisions that eventually reduce system performance. This paper presents an efficient End-to-End (E2E) approach-based unified architecture using Gated Recurrent Units (GRU) for NILM. The proposed approach eliminates explicit feature engineering and has a unified classification and prediction model for appliance power. This eventually reduces the computational cost and enhances response time. The performance of the proposed system is compared with conventional algorithms' with the use of recall, precision, accuracy, F1 score, the relative error in total energy and Mean Absolute Error (MAE). These evaluation metrics are calculated on the power consumption of top priority appliances of Reference Energy Disaggregation Dataset (REDD). The proposed architecture with an overall accuracy of 91.2 and MAE of 25.23 outperforms conventional methods for all electrical appliances. It has been showcased through a series of experiments that feature extraction and event-based approaches for NILM can readily be replaced with E2E deep learning techniques allowing simpler and cost-efficient implementation pathways.
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Singaravelan, Anandakumar, Chung-Ho Hsieh, Yi-Kai Liao, and Jia-Lien Hsu. "Predicting ICD-9 Codes Using Self-Report of Patients." Applied Sciences 11, no. 21 (October 27, 2021): 10046. http://dx.doi.org/10.3390/app112110046.
Повний текст джерелаАнотація:
The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool.
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Zeng, Hong, Chen Yang, Hua Zhang, Zhenhua Wu, Jiaming Zhang, Guojun Dai, Fabio Babiloni, and Wanzeng Kong. "A LightGBM-Based EEG Analysis Method for Driver Mental States Classification." Computational Intelligence and Neuroscience 2019 (September 9, 2019): 1–11. http://dx.doi.org/10.1155/2019/3761203.
Повний текст джерелаАнотація:
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI).
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Wang, Dongjie, Pengyang Wang, Kunpeng Liu, Yuanchun Zhou, Charles E. Hughes, and Yanjie Fu. "Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4410–17. http://dx.doi.org/10.1609/aaai.v35i5.16567.
Повний текст джерелаАнотація:
In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, that produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile. In the experiment, we evaluate our proposed framework on real-world datasets. The extensive experimental results validate the superiority of our method comparing to baseline algorithms.
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Arzo, Sisay Tadesse, Zeinab Akhavan, Mona Esmaeili, Michael Devetsikiotis, and Fabrizio Granelli. "Multi-Agent-Based Traffic Prediction and Traffic Classification for Autonomic Network Management Systems for Future Networks." Future Internet 14, no. 8 (July 28, 2022): 230. http://dx.doi.org/10.3390/fi14080230.
Повний текст джерелаАнотація:
Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best.
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Le, Thi-Thu-Huong, Yongsu Kim, and Howon Kim. "Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks." Applied Sciences 9, no. 7 (April 3, 2019): 1392. http://dx.doi.org/10.3390/app9071392.
Повний текст джерелаАнотація:
The recent increase in hacks and computer network attacks around the world has intensified the need to develop better intrusion detection and prevention systems. The intrusion detection system (IDS) plays a vital role in detecting anomalies and attacks on the network which have become larger and more pervasive in nature. However, most anomaly-based intrusion detection systems are plagued by high false positives. Furthermore, Remote-to-Local (R2L) and User-to-Root (U2R) are two kinds of attack which have low predicted accuracy scores in advance IDS methods. Therefore, this paper proposes a novel IDS framework to overcome these IDS problems. The proposed framework including three main parts. The first part is to build SFSDT model which is the feature selection model. SFSDT is to generate the best feature subset from the original feature set. This model is a hybrid Sequence Forward Selection (SFS) algorithm and Decision Tree (DT) model. The second part is to build various IDS models to train on the best-selected feature subset. The various Recurrent Neural Networks (RNN) are traditional RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Two IDS datasets are used for the learned models in experiments including NSL-KDD in 2010 and ISCX in 2012. The final part is to evaluate the proposed model by comparing the proposed models to other IDS models. The experimental results show the proposed models achieve significantly improved accuracy detection rate as well as attack types classification. Furthermore, this approach can reduce the computation time by memory profilers measurement.
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Proctor, Philippe, Christof Teuscher, Adam Hecht, and Marek Osiński. "Proximal Policy Optimization for Radiation Source Search." Journal of Nuclear Engineering 2, no. 4 (September 30, 2021): 368–97. http://dx.doi.org/10.3390/jne2040029.
Повний текст джерелаАнотація:
Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the advantage actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20×20 m convex and non-convex simulation environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.
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Abdel-Hadi, Aleya, Eman El-Nachar, and Heba Safieldin. "Residents' Perception of Home Range in Cairo." Open House International 36, no. 2 (June 1, 2011): 59–69. http://dx.doi.org/10.1108/ohi-02-2011-b0007.
Повний текст джерелаАнотація:
Recent studies in the realm of housing design avow for the concept of Liveable Cities; an aspect which in turn, places emphasis on the concept of home range. The home range is regarded as the challenge to create a ‘near environment’ that is humanistic and fair, community-oriented and environmentally conscious; a relatively new conception towards responsive and sustainable environments for residents' well-being. Considering that socio-cultural needs in tandem with architectural and urban characteristics correspond to residents perspectives of their home environment; hence, understanding residents' perceptions of their home range should provide designers with deeper insights for creating more responsive residential environments. This study aimed at identifying aspects that contribute to shaping the residents' perception of their home range. The field study included two housing features within the same social class in Egypt with a focus on Cairo: residents of the city's original districts and immigrants of the city to newly suburban gated communities. The methodology was an in-depth qualitative study, exploratory in nature, based on a theoretical content analysis of literature on home range, and a field survey that investigated the residents' perception of the concept. Tools for data gathering relied on photographic and observation methods; together with a structured interview on a random sample in each of the two defined residential environments. Discussions relate findings to planning concepts, and finally, results have generated a framework for decision makers and designers.
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Boers, Tim, Joost van der Putten, Maarten Struyvenberg, Kiki Fockens, Jelmer Jukema, Erik Schoon, Fons van der Sommen, Jacques Bergman, and Peter de With. "Improving Temporal Stability and Accuracy for Endoscopic Video Tissue Classification Using Recurrent Neural Networks." Sensors 20, no. 15 (July 24, 2020): 4133. http://dx.doi.org/10.3390/s20154133.
Повний текст джерелаАнотація:
Early Barrett’s neoplasia are often missed due to subtle visual features and inexperience of the non-expert endoscopist with such lesions. While promising results have been reported on the automated detection of this type of early cancer in still endoscopic images, video-based detection using the temporal domain is still open. The temporally stable nature of video data in endoscopic examinations enables to develop a framework that can diagnose the imaged tissue class over time, thereby yielding a more robust and improved model for spatial predictions. We show that the introduction of Recurrent Neural Network nodes offers a more stable and accurate model for tissue classification, compared to classification on individual images. We have developed a customized Resnet18 feature extractor with four types of classifiers: Fully Connected (FC), Fully Connected with an averaging filter (FC Avg (n = 5)), Long Short Term Memory (LSTM) and a Gated Recurrent Unit (GRU). Experimental results are based on 82 pullback videos of the esophagus with 46 high-grade dysplasia patients. Our results demonstrate that the LSTM classifier outperforms the FC, FC Avg (n = 5) and GRU classifier with an average accuracy of 85.9% compared to 82.2%, 83.0% and 85.6%, respectively. The benefit of our novel implementation for endoscopic tissue classification is the inclusion of spatio-temporal information for improved and robust decision making, and it is the first step towards full temporal learning of esophageal cancer detection in endoscopic video.
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Dutta, Shawni, and Samir Kumar Bandyopadhyay. "Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release." Iberoamerican Journal of Medicine 2, no. 3 (May 12, 2020): 172–77. http://dx.doi.org/10.53986/ibjm.2020.0031.
Повний текст джерелаАнотація:
Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.
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Guo, Yuxue, Xinting Yu, Yue-Ping Xu, Hao Chen, Haiting Gu, and Jingkai Xie. "AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment." Hydrology and Earth System Sciences 25, no. 11 (November 18, 2021): 5951–79. http://dx.doi.org/10.5194/hess-25-5951-2021.
Повний текст джерелаАнотація:
Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.
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Zhang, Zhewei, Huzi Cheng, and Tianming Yang. "A recurrent neural network framework for flexible and adaptive decision making based on sequence learning." PLOS Computational Biology 16, no. 11 (November 3, 2020): e1008342. http://dx.doi.org/10.1371/journal.pcbi.1008342.
Повний текст джерелаАнотація:
The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism’s survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are trained to predict future events based on past events, both comprising sensory, action, and reward events. We show the networks can successfully reproduce animal and human behavior. The networks generalize the training, perform Bayesian inference in novel conditions, and adapt their choices when event contingencies vary. Importantly, units in the network encode task variables and exhibit activity patterns that match previous neurophysiology findings. Our results suggest that the neural network approach based on statistical sequence learning may reflect the brain’s computational principle underlying flexible and adaptive behaviors and serve as a useful approach to understand the brain.
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Balletto, Ginevra, Mara Ladu, Alessandra Milesi, Federico Camerin, and Giuseppe Borruso. "Walkable City and Military Enclaves: Analysis and Decision-Making Approach to Support the Proximity Connection in Urban Regeneration." Sustainability 14, no. 1 (January 1, 2022): 457. http://dx.doi.org/10.3390/su14010457.
Повний текст джерелаАнотація:
Accessibility and urban walkability are the cornerstones of urban policies for the contemporary city, which needs to be oriented towards sustainable development principles and models. Such aims are included in the objectives of the 2030 Agenda, as well as in the ambitious objectives of the ‘European Green Deal’. These concepts are closely linked to the paradigm of a sustainable city—livable, healthy and inclusive—based on a system of high-quality public spaces and on a network of services and infrastructures, both tangible and intangible, capable of strengthening and building new social, economic and environmental relationships. It is necessary to recognize potential opportunities for connection and permeability in consolidated urban environments. These are very often fragmented and are characterized by enclaves of very different kinds. Ghettoes and gated communities, old industrial plants and military installations and facilities, to cite a few, represent examples of cases where closures on urban fabrics are realized, impeding full walkability and accessibility. Within such a framework, the present research is aimed at focusing on a particular set of enclaves, such as those represented by the military sites being reconfigured to civilian use, a phenomenon that characterizes many urban areas in the world; in Europe; and in Italy, in particular, given the recent history and the Cold War infrastructure heritage. In such a sense, the city of Cagliari (Sardinia Island, Italy) represents an interesting case study as it is characterized by the presence of a series of military complexes; real ‘enclaves’ influencing the proximity connections; and, more generally, walkability. Building on previous research and analysis of policies and projects aimed at reintroducing, even partially, this military asset into civilian life (Green Barracks Project (GBP)-2019), this paper proposes and applies a methodology to evaluate the effects of urban regeneration on walkability in a flexible network logic, oriented to the ‘15 min city’ model or, more generally, to the renewed, inclusive, safe “city of proximity”, resilient and sustainable.
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Clark, Hamish P., and Orlando Castellanos Diaz. "Technology Development Framework: Moving from Qualitative toward Quantitative Decision-Making." SPE Journal, November 1, 2022, 1–14. http://dx.doi.org/10.2118/210290-pa.
Повний текст джерелаАнотація:
Summary Technology development in the energy sector typically suffers from an ad hoc approach to making decisions about the key questions of “What is the next derisking step?” and “At what scale?” This lack of structure in decision-making occurs despite the technology development process usually being within a stage-gated protocol. This often leads to significant waste of time and resources and plenty of “surprises” about the extent of additional testing required. The approach proposed in this paper is relatively simple to use, scalable, and transferable across industry sectors. Consistent application of the methodology has the potential to bring a quantitative rigor that can lead to more effective and efficient technology development and improve both intellectual and monetary capital allocation. Additionally, the framework provided facilitates a more structured and faster learning curve for people moving into technology development roles. Starting with a high-level understanding of the technology and business case, the methodology identifies possible commercial deployments and associates key technical and commercial risks that can be barriers to commercialization. Then, using a “risk burndown” approach based around an initial assessment of probability of success (POS), different paths to deployment (PTDs) are developed. A value of information (VOI) approach is used to evaluate key metrics. This leads to a diverse set of derisking options with associated trade-offs for informed decision-making. The methodology has been developed and deployed since 2017 across a portfolio of both incremental improvement and game-changing subsurface technologies at Suncor Energy, a large integrated Canadian energy company. More than 25 technologies have been progressed using elements of this workflow with 7 of the more complex and expensive projects using the full quantitative VOI approach. These analyses have been used to inform decision-making on more than $400 million1 of proposed derisking activity spend.
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Yuan, Junfeng, Jian Wan, Xin Zhang, Yang Xu, Yan Zeng, and Yongjian Ren. "A second-order dynamic and static ship path planning model based on reinforcement learning and heuristic search algorithms." EURASIP Journal on Wireless Communications and Networking 2022, no. 1 (December 30, 2022). http://dx.doi.org/10.1186/s13638-022-02205-4.
Повний текст джерелаАнотація:
AbstractShip path planning plays an important role in the intelligent decision-making system which can provide important navigation information for ship and coordinate with other ships via wireless networks. However, existing methods still suffer from slow path planning and low security problems. In this paper, we propose a second-order ship path planning model, which consists of two main steps, i.e., first-order static global path planning and second-order dynamic local path planning. Specifically, we first create a raster map using ArcGIS. Second, the global path planning is performed on the raster map based on the Dyna-Sarsa($$\lambda$$ λ ) model, which integrates the eligibility trace and the Dyna framework on the Sarsa algorithm. Particularly, the eligibility trace has a short-term memory for the trajectory, which can improve the convergence speed of the model. Meanwhile, the Dyna framework obtains simulation experience through simulation training, which can further improve the convergence speed of the model. Then, the improved ship trajectory prediction model based on stacked bidirectional gated recurrent unit is used to identify the risk of ship collision and switch the path planning from the first order to the second order. Finally, the second-order dynamic local path planning is presented based on the FCC-A* algorithm, where the cost function of the traditional path planning A* algorithm is rewritten using the fuzzy collision cost membership function (fuzzy collision cost, FCC) to reduce the collision risk of ships. The proposed model is evaluated on the Baltic Sea geographic information and ship trajectory datasets. The experimental results show that the eligibility trace and the Dyna learning framework in the proposed model can effectively improve the planning efficiency of the ship’s global path planning, and the collision risk membership function can effectively reduce the number of collisions in A* local path planning and thus improve the navigation safety of encountering ships.
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"Emotor control: computations underlying bodily resource allocation, emotions, and confidence." Emotions 17, no. 4 (December 2015): 391–401. http://dx.doi.org/10.31887/dcns.2015.17.4/akepecs.
Повний текст джерелаАнотація:
Emotional processes are central to behavior, yet their deeply subjective nature has been a challenge for neuroscientific study as well as for psychiatric diagnosis. Here we explore the relationships between subjective feelings and their underlying brain circuits from a computational perspective. We apply recent insights from systems neuroscience—approaching subjective behavior as the result of mental computations instantiated in the brain—to the study of emotions. We develop the hypothesis that emotions are the product of neural computations whose motor role is to reallocate bodily resources mostly gated by smooth muscles. This “emotor” control system is analagous to the more familiar motor control computations that coordinate skeletal muscle movements. To illustrate this framework, we review recent research on “confidence.” Although familiar as a feeling, confidence is also an objective statistical quantity: an estimate of the probability that a hypothesis is correct. This model-based approach helped reveal the neural basis of decision confidence in mammals and provides a bridge to the subjective feeling of confidence in humans. These results have important implications for psychiatry, since disorders of confidence computations appear to contribute to a number of psychopathologies. More broadly, this computational approach to emotions resonates with the emerging view that psychiatric nosology may be best parameterized in terms of disorders of the cognitive computations underlying complex behavior.
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Xuan, Ping, Yu Zhang, Hui Cui, Tiangang Zhang, Maozu Guo, and Toshiya Nakaguchi. "Integrating multi-scale neighbouring topologies and cross-modal similarities for drug–protein interaction prediction." Briefings in Bioinformatics 22, no. 5 (April 12, 2021). http://dx.doi.org/10.1093/bib/bbab119.
Повний текст джерелаАнотація:
Abstract Motivation Identifying the proteins that interact with drugs can reduce the cost and time of drug development. Existing computerized methods focus on integrating drug-related and protein-related data from multiple sources to predict candidate drug–target interactions (DTIs). However, multi-scale neighboring node sequences and various kinds of drug and protein similarities are neither fully explored nor considered in decision making. Results We propose a drug-target interaction prediction method, DTIP, to encode and integrate multi-scale neighbouring topologies, multiple kinds of similarities, associations, interactions related to drugs and proteins. We firstly construct a three-layer heterogeneous network to represent interactions and associations across drug, protein, and disease nodes. Then a learning framework based on fully-connected autoencoder is proposed to learn the nodes’ low-dimensional feature representations within the heterogeneous network. Secondly, multi-scale neighbouring sequences of drug and protein nodes are formulated by random walks. A module based on bidirectional gated recurrent unit is designed to learn the neighbouring sequential information and integrate the low-dimensional features of nodes. Finally, we propose attention mechanisms at feature level, neighbouring topological level and similarity level to learn more informative features, topologies and similarities. The prediction results are obtained by integrating neighbouring topologies, similarities and feature attributes using a multiple layer CNN. Comprehensive experimental results over public dataset demonstrated the effectiveness of our innovative features and modules. Comparison with other state-of-the-art methods and case studies of five drugs further validated DTIP’s ability in discovering the potential candidate drug-related proteins.
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Nykolyuk, Olga, Andrii Lapin, and Inna Hrinchuk. "THE INFORMATION SUPPORT OF PARTICIPANTS OF VERTICALLY INTEGRATED FORMATIONS OF THE FRACTAL TYPE IN AGRIBUSINESS." Economic scope, 2022. http://dx.doi.org/10.32782/2224-6282/179-10.
Повний текст джерелаАнотація:
Fractally organized vertically integrated formations prove one of the main types of vertically integrated business formations which involve agribusiness subjects, thus making it possible to preserve the independence in decision making and self-sufficiency of the corporation members. These formations cover the participants of the chain of creating the agroindustrial produce surplus value. Among the key peculiarities of functioning of such corporations one can single out a high level of the information support and an equal access of their members to the imperative data. The research is aimed at substantiating the peculiarities of the information support, as well as at organizing the network interaction among the participants of vertically integrated formations of the fractal type in agribusiness. The overwhelming majority of the investigations related to the problem of business fractal organization refer to the industrial sector, at the same time, it is the agrarian sector that, on the one hand, acquires all characteristic features of the up-to-date formations possessing the characters of the fractal type business formations, and, on the other hand, they make it possible to ensure the positive effects that are inherent to this very form of business organization. As concerns the problem of the information support within the framework of vertically organized agrarian business systems, they haven't been practically investi-gated, mostly due to the specificity of organizing the network interaction among the participants of vertically integrated formations of the fractal type in agribusiness. The research under study highlights the analysis of business processes within the framework of fractally organized vertically integrated formations. The corresponding functional models are developed. On the basis of the key principles of the systems analysis the author identifies the information flows for every single corporation participant. The author also develops the diagram of the information network of the fractal corporation in accordance with the basic criteria of assessing the degree of protection of the information of computer-based systems. The scientific novelty of the research lies in identifying busi-ness processes in the activities of vertically integrated corporations of the fractal type which are unique for Ukraine. The above processes determine the specific characters of their participants' information support. The results obtained serve as the foundation for the further projection and development of the information system of fractally organized agrarian business formations which provide for the maximum efficiency and competitiveness of all participants of the agrarian pro-duce foodstuff chain.