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Lakhan, Abdullah, Qurat-ul-ain Mastoi, Mazhar Ali Dootio, et al. "Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network." Electronics 10, no. 16 (2021): 1974. http://dx.doi.org/10.3390/electronics10161974.
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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.
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GRUNDY, JOHN. "VISUAL SPECIFICATION AND MONITORING OF SOFTWARE AGENTS IN DECENTRALIZED PROCESS-CENTRED ENVIRONMENTS." International Journal of Software Engineering and Knowledge Engineering 09, no. 04 (1999): 425–44. http://dx.doi.org/10.1142/s0218194099000243.
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Distributed, cooperating software agents are useful in many problem domains, such as task automation and work coordination in process-centered environments. We describe a visual language for specifying such software agents, which uses the composition of event-based software components. These specifications may contain interfaces to remotely executing agents, and agents may be run locally or on distributed machines using a decentralized software architecture. As facilities to configure and monitor the state and activities of such distributed, cooperating software agents is essential, we provide primarily visual capabilities to achieve this. Our static and dynamic software agent visualization techniques have been used on several projects where distributed information processing, system interfacing, work coordination and task automation are required. We illustrate our visualization techniques with examples from these domains.
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García, Sergi, Selen Razon, Robert Hristovski, Natàlia Balagué, and Gershon Tenenbaum. "Dynamic Stability of Task-Related Thoughts in Trained Runners." Sport Psychologist 29, no. 4 (2015): 302–9. http://dx.doi.org/10.1123/tsp.2014-0094.
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Drawing upon the nonlinear model of attention focus, the purpose of this study was to compare the intrinsic and intentional dynamics of task-related thoughts (TRT) in trained runners and nonrunners during an incremental maximal test. Fourteen trained runners and 14 nonrunners were assigned to 2 conditions: intrinsic (nonimposed thoughts) and intentional (imposed, task-unrelated thoughts; TUT). A significant effect of running velocity over TUT/TRT dynamics in both groups and conditions was observed (p < .001). Although, all participants received instructions to keep TUT for the entire duration of the test, an initially stable TUT phase was followed by a metastable phase (i.e., switches between TUT and TRT) an a final stable TRT phase nearing volitional exhaustion. The stable TRT phase lasted longer in runners group (p < .05) and included higher probabilities in pace monitoring thoughts subcategory (p < .05). The results revealed that trained runners seem to use TRT (i.e., pace monitoring) to maximize performance, and confirm the nonlinear model of attention focus during incremental maximal run in trained runners and nonrunners.
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Chen, Feng Ping, Li Miao, and Yue Gao Tang. "Research of Hadoop Parameters Tuning Based on Function Monitoring." Applied Mechanics and Materials 621 (August 2014): 264–70. http://dx.doi.org/10.4028/www.scientific.net/amm.621.264.
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Hadoop is a popular software framework supports distributed processing of large data sets. However, with Hadoop being a relatively new technology, practitioners and administers often lack the expertise to tune it to get better performance. Hadoop parameters configuration is one of the key factors which influence the performance. In this article, we present a novel Hadoop parameters tuning method based on function monitoring. This method monitors the function call information during task run to analyze why the performance of Hadoop changes when tuning parameters, which will be helpful for practitioners and administer to tune parameters to get better performance.
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Shahriar, Hossain, and Mohammad Zulkernine. "Monitoring Buffer Overflow Attacks." International Journal of Secure Software Engineering 1, no. 3 (2010): 18–40. http://dx.doi.org/10.4018/jsse.2010070102.
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Buffer overflow (BOF) is a well-known, and one of the worst and oldest, vulnerabilities in programs. BOF attacks overwrite data buffers and introduce wide ranges of attacks like execution of arbitrary injected code. Many approaches are applied to mitigate buffer overflow vulnerabilities; however, mitigating BOF vulnerabilities is a perennial task as these vulnerabilities elude the mitigation efforts and appear in the operational programs at run-time. Monitoring is a popular approach for detecting BOF attacks during program execution, and it can prevent or send warnings to take actions for avoiding the consequences of the exploitations. Currently, there is no detailed classification of the proposed monitoring approaches to understand their common characteristics, objectives, and limitations. In this paper, the authors classify runtime BOF attack monitoring and prevention approaches based on seven major characteristics. Finally, these approaches are compared for attack detection coverage based on a set of BOF attack types. The classification will enable researchers and practitioners to select an appropriate BOF monitoring approach or provide guidelines to build a new one.
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Naik, K. Jairam, and D. Hanumanth Naik. "Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing." Scalable Computing: Practice and Experience 21, no. 1 (2020): 73–84. http://dx.doi.org/10.12694/scpe.v21i1.1616.
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Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than somecommon algorithms such as active monitoring, weighted round robin and throttled load balancer.
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Bukit, Alexander Victor, I. Nengah Putra A, Febryan Galih Rahardi, and Ahmadi Ahmadi. "DESIGN OF TEMPERATURE AND AIR HUMIDITY MONITORING INFORMATION SYSTEM BASED ON ANDROID AND IOT (INTERNET OF THINGS)." JOURNAL ASRO 11, no. 2 (2020): 102. http://dx.doi.org/10.37875/asro.v11i2.273.
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For a soldier, physical abilities are needed in every task implementation, so the daytime running activities become a compulsory menu for every soldier who is carrying out education. Both the basic military education and further education. According to health experts, benefit of jogging is to lose weight so as to avoid obesity, improve the ability of the lungs, heart and various other health benefits. However, we also have to pay attention to the temperature of the air because it is at a certain temperature, it will be very risky if we carry out the afternoon run. Not a few victims affected by heat stroke due to run during the day with high air temperatures. The current mechanism, temperature measurements are carried out manually by members of the local Satkes, then the Satkes report the results of temperature measurements to Binjas and then Binjas decide whether or not to run afternoons that day. After the decision was made, the Binjas reported to the guard division and the guard to spread the news through the prayer watch. Therefore, with this android-based temperature and humidity monitoring information system, we can easily find out whether today's temperature is safe or not to carry out daytime running activities.Keywords : Afternoon Run, Heat stroke, Android.
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Kaminka, G. A., D. V. Pynadath, and M. Tambe. "Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach." Journal of Artificial Intelligence Research 17 (September 1, 2002): 83–135. http://dx.doi.org/10.1613/jair.970.
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Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' *routine* communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.
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Jade, Ankita, Nikita Bhirud, Gauri Patwari, and Ankita Vaste. "Identification and Detection of Missing RFID Tags using RUN Protocol." International Journal Of Engineering And Computer Science 7, no. 02 (2018): 23531–34. http://dx.doi.org/10.18535/ijecs/v7i2.04.
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As human race or human society is growing, the wildlife animals or wild animals are in danger. But as per natures rule, every living creature on this earth is important and has important role ecosystem. The domestic animals get misplaced sometimes and finding them is a tedious task. RFID and sensors have been deployed to detect and identify missing animals by affixing them with cheap passive RFID tag and monitoring them with RFID readers. So the proposed system will help us to detect and find the missing animals using RUN protocol. RUN protocol uses slotted aloha for communication between tags and readers. It execute multiple frame for different seeds to reduce the effect of unexpected tags and also it reduces the time of missing tag detection and identification. To obtain optimal frame sizes and minimum no of times aloha frames should be executed to mitigate the effect of unexpected tags. RUN protocol works with multiple readers with overlapping region. It identifies 100% missing tags in the presence of unexpected tags where as other protocol only identifies 60% of missing tags.
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Dolev, Shlomi, Jonathan Goldfeld, Rami Puzis, and Muni Venkateswarlu K. "Efficient online detection of temporal patterns." PeerJ Computer Science 2 (April 13, 2016): e53. http://dx.doi.org/10.7717/peerj-cs.53.
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Identifying a temporal pattern of events is a fundamental task of online (real-time) verification. We present efficient schemes for online monitoring of events for identifying desired/undesired patterns of events. The schemes use preprocessing to ensure that the number of comparisons during run-time is minimized. In particular, the first comparison following the time point when an execution sub-sequence cannot be further extended to satisfy the temporal requirements halts the process that monitors the sub-sequence.
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Wightman, Andrew, Geoffrey Smith, Kelci Mohrman, and Charles Mueller. "Trigger Rate Monitoring Tools at CMS." EPJ Web of Conferences 214 (2019): 01047. http://dx.doi.org/10.1051/epjconf/201921401047.
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One of the major challenges for the Compact Muon Solenoid (CMS)experiment, is the task of reducing event rate from roughly 40 MHz down to a more manageable 1 kHz while keeping as many interesting physics events as possible. This is accomplished through the use of a Level-1 (L1) hardware based trigger as well as a software based High-Level Trigger (HLT). Monitoring and understanding the output rates of the L1 and HLT triggers is of key importance for determining the overall performance of the trigger system and is intimately tied to what type of data is being recorded for physics analyses. We present here a collection of tools used by CMS to monitor the L1 and HLT trigger rates. One of these tools is a script (run in the CMS control room) that gives valuable real-time feedback of trigger rates to the shift crew. Another useful tool is a plotting library, that is used for observing how trigger rates vary over a range of beam and detector conditions, in particular how the rates of individual triggers scale with event pile-up.
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Dattel, Andrew R., Stephen M. Popkin, and John K. Pollard. "Effectiveness of Cognitive-Based and Conventional Alerters for Locomotive Engineers." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 22 (2002): 1829–33. http://dx.doi.org/10.1177/154193120204602211.
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This is the first phase of a three-phase study that examined the effectiveness and reliability of alertness monitoring and warning devices for locomotive engineers. Four university students participated in this initial phase. Two experimental sessions were run for each participant, once during dayshift hours (9am to either 1pm or 5pm) and once during nightshift hours (10pm until 2am or 6am). They completed a series of vigilance performance tasks throughout their 4-hour or 8-hour experimental session. A conventional electro-mechanical alerter commonly found on board a locomotive was used for one of the two sessions, while a novel simple cognitive-based task was used for the other session. The Psychomotor Vigilance Test (PVT) and percent eye closure (PERCLOS) were used as measures of alertness. As expected, PERCLOS and performance task reaction time (RT) increased throughout the night sessions. Furthermore, it was found that when participants were exposed to the cognitive-based alerter, they had lower PERCLOS values throughout the night sessions and shorter vigilance task RT throughout the day sessions than when they were exposed to the conventional alerter. These results are discussed with respect to adaptive automation and their potential implications.
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Rahim, Endang, and Roni Mohamad. "STRATEGI BAURAN PEMASARAN ( MARKETING MIX ) DALAM PERSPEKTIF SYARIAH." MUTAWAZIN (Jurnal Ekonomi Syariah) 2, no. 1 (2021): 15–26. http://dx.doi.org/10.54045/mutawazin.v2i1.234.
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Business activities are activities that require serious understanding, starting from planning what will be done, the implementation stage and the stage of monitoring a business being run. In running a business, the important task of marketers is to be able to provide what is needed by the community, both consumers and customers. In order for a business or business that is run to be successful, it would be nice to have an appropriate competitive strategy. This study aims to provide a study and analysis of the marketing mix strategy (Marketing Mix) in the sharia perspective. This type of research is a literature study, data is collected by conducting a review study of previous research journals that are relevant to this research.
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He, Jialuan, Zirui Xing, Tianqi Xiang, et al. "Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring." Sensors 21, no. 17 (2021): 5688. http://dx.doi.org/10.3390/s21175688.
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In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.
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Okafor, O. J., O. C. Nwokonkwo, and A. M. John-Otumu. "An Integrated Model for Monitoring Nodes in Computer Networks." Journal of Advances in Computing, Communications and Information Technology 1 (April 25, 2021): 18–27. http://dx.doi.org/10.37121/jaccit.v1.154.
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Monitoring complex computer network environment is now a very challenging task for network administrators despite the various existing monitoring applications for networks that are faced with the issues of centralized monitoring, which causes network traffic, reduces network bandwidth, and are unable to concurrently run two or more network services. This research paper was designed to tackle the problems exhibited by the existing network monitoring application by integrating different network monitoring services in a single model using the power of agent’s distributed processing and monitoring services. Data about the existing and proposed model was gathered using key informant interview approach, and observation of the existing software. Iterative software model was adopted as the software development life cycle based on its strengths and suitability. The proposed model was developed using use-case and sequence diagrams. Suitable programming languages and development environment such as Java, JavaScript, Hypertext Preprocessor, Hypertext markup language and MySQL were used in coding the software prototype. The functionality of the proposed system was tested and results showed that the proposed system has 100% anomaly network intrusion detection rate and better functional features as compared to the existing network monitoring applications observed.
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Benabdelhafid, Maya Souilah, and Mahmoud Boufaida. "The Need for Formal Compatibility Analysis in Web Service Choreography via an E-Commerce Application." International Journal of E-Business Research 11, no. 4 (2015): 1–16. http://dx.doi.org/10.4018/ijebr.2015100101.
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The formal analysis of Web service compatibility is an important task that is not supported by current composition languages, due to their lack of well-defined formal semantics. This paper addresses the raised issues by providing a survey of service composition with focus on formal compatibility analysis and proposing an e-commerce application that integrates formal methods in Web service choreographies. These methods, including Colored Petri Nets (CPNs) and Timed CPNs, are used at design-time for analyzing service compatibility that will be monitored at run-time. This paper forms an important step towards reliable choreographies since by analyzing at design-time problems could be detected early in the development cycle before starting the implementation. It discusses the monitoring of the compatibility evolution at run-time. The effectiveness of our work is validated using CPN Tools.
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Scapaticci, Rosa, Gennaro G. Bellizzi, Marta Cavagnaro, Vanni Lopresto, and Lorenzo Crocco. "Exploiting Microwave Imaging Methods for Real-Time Monitoring of Thermal Ablation." International Journal of Antennas and Propagation 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/5231065.
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Microwave thermal ablation is a cancer treatment that exploits local heating caused by a microwave electromagnetic field to induce coagulative necrosis of tumor cells. Recently, such a technique has significantly progressed in the clinical practice. However, its effectiveness would dramatically improve if paired with a noninvasive system for the real-time monitoring of the evolving dimension and shape of the thermally ablated area. In this respect, microwave imaging can be a potential candidate to monitor the overall treatment evolution in a noninvasive way, as it takes direct advantage from the dependence of the electromagnetic properties of biological tissues from temperature. This paper explores such a possibility by presenting a proof of concept validation based on accurate simulated imaging experiments, run with respect to a scenario that mimics an ex vivo experimental setup. In particular, two model-based inversion algorithms are exploited to tackle the imaging task. These methods provide independent results in real-time and their integration improves the quality of the overall tracking of the variations occurring in the target and surrounding regions.
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Neuhausen, Marcel, Dennis Pawlowski, and Markus König. "Comparing Classical and Modern Machine Learning Techniques for Monitoring Pedestrian Workers in Top-View Construction Site Video Sequences." Applied Sciences 10, no. 23 (2020): 8466. http://dx.doi.org/10.3390/app10238466.
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Keeping an overview of all ongoing processes on construction sites is almost unfeasible, especially for the construction workers executing their tasks. It is difficult for workers to concentrate on their work while paying attention to other processes. If their workflows in hazardous areas do not run properly, this can lead to dangerous accidents. Tracking pedestrian workers could improve the productivity and safety management on construction sites. For this, vision-based tracking approaches are suitable, but the training and evaluation of such a system requires a large amount of data originating from construction sites. These are rarely available, which complicates deep learning approaches. Thus, we use a small generic dataset and juxtapose a deep learning detector with an approach based on classical machine learning techniques. We identify workers using a YOLOv3 detector and compare its performance with an approach based on a soft cascaded classifier. Afterwards, tracking is done by a Kalman filter. In our experiments, the classical approach outperforms YOLOv3 on the detection task given a small training dataset. However, the Kalman filter is sufficiently robust to compensate for the drawbacks of YOLOv3. We found that both approaches generally yield a satisfying tracking performances but feature different characteristics.
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Ramesh, Pasupuleti, and Uppu Ramachandraiah. "E-Token Energy-Aware Proportionate Sharing Scheduling Algorithm for Multiprocessor Systems." Wireless Communications and Mobile Computing 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/8382026.
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WSN plays vital role from small range healthcare surveillance systems to largescale environmental monitoring. Its design for energy constrained applications is a challenging issue. Sensors in WSNs are projected to run separately for longer periods. It is of excessive cost to substitute exhausted batteries which is not even possible in antagonistic situations. Multiprocessors are used in WSNs for high performance scientific computing, where each processor is assigned the same or different workload. When the computational demands of the system increase then the energy efficient approaches play an important role to increase system lifetime. Energy efficiency is commonly carried out by using proportionate fair scheduler. This introduces abnormal overloading effect. In order to overcome the existing problems E-token Energy-Aware Proportionate Sharing (EEAPS) scheduling is proposed here. The power consumption for each thread/task is calculated and the tasks are allotted to the multiple processors through the auctioning mechanism. The algorithm is simulated by using the real-time simulator (RTSIM) and the results are tested.
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Assuncao, Luis, Carlos Goncalves, and Jose C. Cunha. "Autonomic Workflow Activities." International Journal of Adaptive, Resilient and Autonomic Systems 5, no. 2 (2014): 57–82. http://dx.doi.org/10.4018/ijaras.2014040104.
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Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from workflow tasks specification, decentralizing the control of workflow activities, and allowing their tasks to run autonomous in distributed infrastructures, for instance on Clouds. Furthermore many workflow tools only support the execution of Direct Acyclic Graphs (DAG) without the concept of iterations, where activities are executed millions of iterations during long periods of time and supporting dynamic workflow reconfigurations after certain iteration. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on the Process Networks model, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures, e. g. on Clouds. Each AWA executes a Task developed as a Java class that implements a generic interface allowing end-users to code their applications without concerns for low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables support to dynamic workflow reconfiguration and monitoring of the execution of workflows. We describe how AWARD supports dynamic reconfiguration and discuss typical workflow reconfiguration scenarios. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to a small dedicated cluster and the Amazon (Elastic Computing EC2) Cloud.
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Alier, Marc, Maria Jose Casañ Guerrero, Daniel Amo, Charles Severance, and David Fonseca. "Privacy and E-Learning: A Pending Task." Sustainability 13, no. 16 (2021): 9206. http://dx.doi.org/10.3390/su13169206.
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Most educational software programs use and gather personal information and metadata from students. Additionally, most of the educational software programs are no longer operated by the learning institutions but are run by third-party agencies. This means that in the decade since 2020, information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems and online learning tools is increasingly in custody of cloud computing platforms, software-as-a-service providers, and learning tool vendors. There is an increasing will to use all the data and metadata from the activity of the students for research, to develop education management strategies, pedagogy approaches, and develop behavior control tools or learning tools informed by behavior analysis from learning analytics. Many times, these studies lack the ethical and moral perspective. In addition, there is an increasing number of cases in which this information has leaked or has been used in a shady way. Additionally, this information will be around for a long time, tied to the future digital profiles of the students whose data has been leaked. This paper hypothesizes that there has been an ongoing process of technological evolution that leads to a loss of control over personal information, which makes it even more difficult to protect user confidentiality and ensuring privacy, that data surveillance has entered the world of education, and that the current legal frameworks are not enough to really protect the student’s personal information. The paper analyzes how this situation came to pass, and why this is wrong. We conclude with some proposals to address it from its different root dimensions: technical, cultural, legal, and organizational.
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Pasha, Muhammad Adeel, Jahangir Hassan Khan, and Shahid Masud. "I-LEACH: energy-efficient routing protocol for monitoring of irrigation canals." SIMULATION 91, no. 8 (2015): 750–64. http://dx.doi.org/10.1177/0037549715602957.
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Irrigation canals and their tributaries, originating from the run of a river, span a considerable geographical area that is typically of the order of 100 km2. Efficient deployment of wireless sensor networks (WSNs) to monitor and control the flow of scarce water resources is a challenging task, mainly because the WSN motes are designed to consume very low power and communicate over very short distances. This paper investigates the use of different WSN protocols to monitor water usage over irrigation networks in the province of Punjab, Pakistan. This irrigation network is considered to be among the biggest in the world with over 58,000 canal outlets. The paper proposes a new energy-efficient routing protocol that improves the network lifespan as well as the packet error rates resulting in a more dependable distributed WSN. The protocol was simulated along with other state-of-the-art protocols in random as well as linear topologies with simulation variables such as the number of nodes, coverage area and base station position varied in each case. The results also show that the node die-out pattern in our protocol is uniformly spread throughout the network as compared with other existing protocols resulting in increased robustness. The NS3 network simulator was used to simulate the geographically distributed WSN with different protocols and system parameters.
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Prince, Peter, Andrew Hill, Evelyn Piña Covarrubias, Patrick Doncaster, Jake Snaddon, and Alex Rogers. "Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring." Sensors 19, no. 3 (2019): 553. http://dx.doi.org/10.3390/s19030553.
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Conservation researchers require low-cost access to acoustic monitoring technology. However, affordable tools are often constrained to short-term studies due to high energy consumption and limited storage. To enable long-term monitoring, energy and space efficiency must be improved on such tools. This paper describes the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring on affordable, open-source hardware. The algorithms aim to detect bat echolocation, to search for evidence of an endangered cicada species, and also to collect evidence of poaching in a protected nature reserve. The algorithms are designed to run on AudioMoth: a low-cost, open-source acoustic monitoring device, developed by the authors and widely adopted by the conservation community. Each algorithm addresses a detection task of increasing complexity, implementing extra analytical steps to account for environmental conditions such as wind, analysing samples multiple times to prevent missed events, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm, we report on real-world deployments carried out with partner organisations and also benchmark the hidden Markov model against a convolutional neural network, a deep-learning technique commonly used for acoustics. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.
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Zhou, Chenguang, Wei Gao, Jiarui Hu, Liangmin Du, and Lin Du. "Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events." Remote Sensing 13, no. 4 (2021): 689. http://dx.doi.org/10.3390/rs13040689.
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The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L.
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Bian, Pei Ying. "Design of Tools Flow Based on Reusable Scheduling in FMS." Advanced Materials Research 744 (August 2013): 491–94. http://dx.doi.org/10.4028/www.scientific.net/amr.744.491.
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According to importance of the tool in the modern production,on the basis of tool-flow analysis, this thesis establishes reusable scheduling strategy. In this scheduling, it established a tool information management system based on the central tools magazine. The system includes the main information of each of the tools such as: number, location, usage, current state etc, and for them makes on real-time monitoring and anomalies reporting. Besides a priority mechanism is formulated for multi-tool and multi-task in the scheduling. Finally, the reusable scheduling has been modeling and simulation. To run the emulation program proved that the real-time and effectiveness of the tool management system, and the result of running is analyzed under configurations include the number of different tool, which for the actual production scheduling of tool provides the algorithms and software platforms.
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Venkata Ramana, D., and S. Baskar. "Incipient Fault Detection of the Inverter Fed Induction Motor Drive." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (2017): 722. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp722-729.
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Inverter fed Induction motor drives are deployed across a variety of industrial and commercial applications. Although the drives in the question are well known for their reliable operation in any type of environment, it becomes an important daunting critical task to have them in continuous operation as per the applications’ requirement. Identifying the faulty behavior of power electronic circuits which could lead to catastrophic failures is an attractive proposition. The cost associated with building systems devoted for monitoring and diagnosis is high, however such cost could be justified for the safety-critical systems. Commonly practiced methods for improving the reliability of the power electronic systems are: designing the power circuit conservatively or having parallel redundant operation of components or circuits and clearly these two methods are expensive. An alternative to redundancy is fault tolerant control, which involves drive control algorithm, that in the event of fault occurrence, allows the drive to run in a degraded mode. Such algorithms involve on-line processing of the signals and this requires Digital Signal Processing of the signals. This paper presents the FFT and Wavelet transform techniques for on-line monitoring and analyzing the signals such as stator currents.
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Araujo, Gustavo Silva, and José Valentim Vicente. "Estimação da Inflação Implícita de Curto Prazo." Brazilian Review of Finance 15, no. 2 (2018): 227. http://dx.doi.org/10.12660/rbfin.v15n2.2017.68807.
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Implicit inflation or break-even inflation rate (BEIR) is the difference between nominal and real interest rates. In the Brazilian market, we can obtain it from indexed government bonds. However, when dealing with short-term BEIR, this task presents two difficulties: a) inflation-indexed bonds have indexation lags; b) inflation seasonality implies real interest rate seasonality. The aim of this paper is to propose a methodology to estimate the short-term BEIR that addresses these two issues. Assuming a negligible inflation risk premium in the short run, we evaluate the predictive ability of the BEIR by confronting it with expectations based on the market analysts’ forecasts published on the Focus Survey. The results show that the BEIR is competitive when compared to the Focus Survey. An advantage of the BEIR is that it allows monitoring of expectations better than surveys, since it is continuously updated.
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Barroso, Vasco Chibante, Domenico Elia, Costin Grigoras, Andres Gomez Ramirez, Gioacchino Vino, and Adam Wegrzynek. "Towards the integrated ALICE Online-Offline (O2) monitoring subsystem." EPJ Web of Conferences 214 (2019): 03043. http://dx.doi.org/10.1051/epjconf/201921403043.
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ALICE (A Large Ion Collider Experiment) is preparing for a major upgrade of the detector, readout and computing systemsfor LHC Run 3. A new facility called O2 (Online-Offline) will play a major role in data compression and event processing. To efficiently operate the experiment, we are designing a monitoring subsystem, which will provide a complete overview of the O2 overall health, detect performance degradation and component failures. The monitoring subsystem will receive and collect up to 600 kHz of performance metrics. It consists of a custom monitoring library and a server-side, distributed software covering five main functional tasks: parameter collection and processing, storage, visualisation and alarms. To select the most appropriate tools for these tasks, we evaluated three options: “Modular Stack”, Zabbix and the currently used ALICE Grid monitoring tool called MonALISA. The former one consists of a toolkit including collectd, Apache Flume, Apache Spark, InfluxDB, Grafana and Riemann. This paper describes the monitoring subsystem functional architecture. It goes through a complete evaluation of the three considered options, the selection process, risk assessment and justification for the final decision. The in-depth comparison includes functional features and throughput measurement to ensure the required processing and storage performance.
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Berenjian, Golnaz, Homayun Motameni, Mehdi Golsorkhtabaramiri, and Ali Ebrahimnejad. "Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 251–72. http://dx.doi.org/10.3233/jifs-201696.
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Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.
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Sukmana, Farid, and Fahrur Rozi. "Digitalization In Scanning And Remote Shutdown Host of Computer Using Apache Cordova Framework." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 4, no. 2 (2019): 88. http://dx.doi.org/10.29100/jipi.v4i2.1122.
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<p class="Abstract">Internet of Things is one concept in technology 4.0, this technology combine of two component, that is internet and mechine. Internet use to send signal from mechine to second device like mobilephone or from the mobilephone to mechine to execution task. Information Technnology infrastructure in industrial company has many complexity in network, hardware, or software. Especially for hardware like desktop computer or laptop, ech employee has one of them. And many of them, sometime forget to shutdown they computer or they has purpose did not shutdown they computer just for download the big file. Indirectly, the computer that run more than 24 hours in industrial company, it will be degrade service of life, and the consumption of electricity need the high cost. In this paper, our purpose is how to reduce computer that not needed run 24 hours can be monitor and can be shutdown in the out of company area. This is need concept of internet of things and digitalitation to make it. For this problem can be used monitoring system by mobile application, this system will be show about the host computer still be on, and can be remote shutdown with mobile device. This application use cordova to build it and the command from mobile be send to server to shutdown computer that still online. </p>
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Yang, Zhongguo, Irshad Ahmed Abbasi, Elfatih Elmubarak Mustafa, Sikandar Ali, and Mingzhu Zhang. "An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm." Security and Communication Networks 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/6677027.
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Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data’s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently.
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Russell-Buckland, Joshua, Matthew Caldwell, and Ilias Tachtsidis. "WeBCMD: A cross-platform interface for the BCMD modelling framework." Wellcome Open Research 2 (July 26, 2017): 56. http://dx.doi.org/10.12688/wellcomeopenres.12201.1.
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Multimodal monitoring of the brain generates a great quantity of data, providing the potential for great insight into both healthy and injured cerebral dynamics. In particular, near-infrared spectroscopy can be used to measure various physiological variables of interest, such as haemoglobin oxygenation and the redox state of cytochrome-c-oxidase, alongside systemic signals, such as blood pressure. Interpreting these measurements is a complex endeavour, and much work has been done to develop mathematical models that can help to provide understanding of the underlying processes that contribute to the overall dynamics. BCMD is a software framework that was developed to run such models. However, obtaining, installing and running this software is no simple task. Here we present WeBCMD, an online environment that attempts to make the process simpler and much more accessible. By leveraging modern web technologies, an extensible and cross-platform package has been created that can also be accessed remotely from the cloud. WeBCMD is available as a Docker image and an online service.
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NALUTSYSHYN, Viktor V., Vasyl M. YURCHYSHYN, and Vasyl V. CHEREPUSHCHAK. "Scientific and Legal Aspect for Conducting of Operational Measures to Counter Threats of Terrorist Acts upon Their Commission as a Challenge to the Social Community." Journal of Advanced Research in Law and Economics 10, no. 7 (2019): 2060. http://dx.doi.org/10.14505/jarle.v10.7(45).17.
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Terrorist threat act may lie not only in potential civilian casualties, but also in the investigation of acts that could in the long run lead to the implementation of terrorist intentions. The relevance of the study is determined by the fact that the possibility of preventing a terrorist act should be in the interdepartmental jurisdiction of law enforcement agencies not only as a task of emergency response, but also as a task of direct monitoring. The novelty of the study is determined by the fact that the authors consider the aspect of the formation of countermeasures against a terrorist act on the basis of liquidating the foundation of its social response. The very process of preventing a terrorist act predominantly affects only measures to destroy terrorists and reduce the number of potential victims. The paper demonstrates that in addition to this postulate, it is worth considering the need for an additional reduction in social resonance, which acts as an incentive. Formation of counteraction by law enforcement agencies allows to reduce the significance of the terrorist impact on society. The practical significance of the study is determined by the fact that each of the participants in countering the terrorist act is also capable of operative activities, which can also additionally level the structure for implementing the provisions of anti-terrorism legislation.
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Porter, Blake S., Kristin L. Hillman, and David K. Bilkey. "Anterior cingulate cortex encoding of effortful behavior." Journal of Neurophysiology 121, no. 2 (2019): 701–14. http://dx.doi.org/10.1152/jn.00654.2018.
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An animal’s ability to assess the value of their behaviors to minimize energy use while maximizing goal achievement is critical to its survival. The anterior cingulate cortex (ACC) has been previously shown to play a critical role in this behavioral optimization process, especially when animals are faced with effortful behaviors. In the present study, we designed a novel task to investigate the role of the ACC in evaluating behaviors that varied in effort but all resulted in the same outcome. We recorded single unit activity from the ACC as rats ran back and forth in a shuttle box that could be tilted to different tilt angles (0, 15, and 25°) to manipulate effort. Overall, a majority of ACC neurons showed selective firing to specific effort conditions. During effort expenditure, ACC units showed a consistent firing rate bias toward the downhill route compared with the more difficult uphill route, regardless of the tilt angle of the apparatus. Once rats completed a run and received their fixed reward, ACC units also showed a clear firing rate preference for the single condition with the highest relative value (25° downhill). To assess effort preferences, we used a choice version of our task and confirmed that rats prefer downhill routes to uphill routes when given the choice. Overall, these results help to elucidate the functional role of the ACC in monitoring and evaluating effortful behaviors that may then bias decision-making toward behaviors with the highest utility. NEW & NOTEWORTHY We developed a novel effort paradigm to investigate how the anterior cingulate cortex (ACC) responds to behaviors with varied degrees of physical effort and how changes in effort influence the ACC’s evaluation of behavioral outcomes. Our results provide evidence for a wider role of the ACC in its ability to motivate effortful behaviors and evaluate the outcome of multiple behaviors within an environment.
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Ehigiator, R. Irughe, J. O. Ehiorobo, M. O. Ehigiator, and Ashraf A. Beshr. "Determining the Subsidence of Oil Storage Tank Walls from Geodetic Leveling." Advanced Materials Research 367 (October 2011): 467–74. http://dx.doi.org/10.4028/www.scientific.net/amr.367.467.
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In this paper the monitoring for subsidence in crude oil storage tanks by the method of Geodetic leveling is discussed. The monitoring network consisted of three control points established about 100m from the tank and 16 studs established at the base of the tank. From the control points, the stud locations were leveled using a geodetic level with parallel plate micrometer and telescopic staves. All levels were run in forward and reverse directions and the measurements were carried out in 2003, 2004 and 2008. Adjustment of observation was carried out using the least squares estimation model to determine the elevation of each stud position in the three measurement epochs together with their accuracy standards. Comparisons were made of the calculated movements from the three measurement epochs and the associated accuracies calculated from the least squares model. Analysis of the results indicated that with the exception of one stud ( stud 8), all other studs emplaced had moved and the movements ranged from 0.91mm to 13.06mm
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Anggraeni, Diah. "Bureaucratic Reform in the Empirical Perspective and Pathology." International Journal of Kybernology 3, no. 2 (2019): 1–14. http://dx.doi.org/10.33701/ijok.v3i2.588.
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Bureaucracy in government organizations is a key factor in the implementation of dailyadministration tasks. The division of labor, authority and responsibility and supervision,will be evident in the structure of this organization. Errors in the preparation of thestructure will be able to lead the implementation of the task can not be run efficiently andeffectively, and may even lead to errors in the performance of duties. And these errorsare related to the attitude of the bureaucrats’ condition urgent reforms. Wisdom Reformsto be implemented need to be identified on the substance of wisdom in it that includesnine (9) main program (Ministry of PAN-RB, 2010b: 22-26), namely: Change ManagementProgram; Setup program legislation; Organization Structuring and ReinforcementProgram; Setup Program Management; System Setup program in Human ResourceManagement Apparatus; Strengthening Monitoring Program; Strengthening ProgramPerformance Accountability; Public Services Quality Improvement Program; ProgramMonitoring, Evaluation and Reporting related to the reform of the bureaucracy tends tochange the bureaucratic standard. Pathology is a perception that attitudes, behaviors,and as a tendency to respond to a person’s soul something that is operationalized throughbehavior. Behavior will affect the commitment and dedication of personnel in performanceof its duties, especially in service to the community. In this case, which is an indicator ofthe attitude dimensions executor is a personal interest, affection for the organization, andforesight.Key Words: Bureaucracy Reform, Bureaucracy Pathology, Sustainable Development.
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Sunarta, Sunarta, Alexander Victor Bukit, Arie Marbandi, and Kusdianto Kusdianto. "DESIGN OF INDONESIAN NAVY PERSONAL EQUIPMENT MONITORING SYSTEM BASED ON ANDROID MODEL." JOURNAL ASRO 11, no. 2 (2020): 90. http://dx.doi.org/10.37875/asro.v11i2.272.
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Kaporlap is individual equipment that is attached to the body of a soldier, including all accessories, ranging from headgear, body cover to leg cover. These individual equipment were distributed so that each soldier could carry out basic tasks or routine daily tasks and the additional tasks provided could run well. Design and Development of the Navy-based College of Technology Kaporlap Monitoring System (STTAL) Android-based is an information system designed to make it easier for Kaur warehouse to make a report to the Kadeprenalog to find out stock information available at Renalog warehouse when it will distribute Kaporlap to Personnel and Students. Data collection methods used in the preparation of this final project are field studies and literature. After being identified, the authors designed the Kaporlap inventory system with systems development models such as context diagrams, Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD) through to Input and Output designs, then proceed with the making of its web program and after that it can uploaded to the android system. After the whole system process is complete, it can be seen that the inventory system can produce information for official needs accurately and quickly.Keywords : Kaporlap Inventory System, Web Data, Android Applications.
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Michailidou, Christina, Vasileios Gkioulos, Andrii Shalaginov, Athanasios Rizos, and Andrea Saracino. "RESPOnSE—A Framework for Enforcing Risk-Aware Security Policies in Constrained Dynamic Environments." Sensors 20, no. 10 (2020): 2960. http://dx.doi.org/10.3390/s20102960.
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The enforcement of fine-grained access control policies in constrained dynamic networks can become a challenging task. The inherit constraints present in those networks, which result from the limitations of the edge devices in terms of power, computational capacity and storage, require an effective and efficient access control mechanism to be in place to provide suitable monitoring and control of actions and regulate the access over the resources. In this article, we present RESPOnSE, a framework for the specification and enforcement of security policies within such environments, where the computational burden is transferred to high-tier nodes, while low-tier nodes apply risk-aware policy enforcement. RESPOnSE builds on a combination of two widely used access control models, Attribute-Based Access Control and Role-Based Access Control, exploiting the benefits each one provides. Moreover, the proposed mechanism is founded on a compensatory multicriteria decision-making algorithm, based on the calculation of the Euclidean distance between the run-time values of the attributes present in the security policy and their ideal values, as those are specified within the established policy rules.
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Charchalis, Adam, and Mirosław Dereszewski. "Processing of Instantaneous Angular Speed Signal for Detection of a Diesel Engine Failure." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/659243.
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Continuous monitoring of diesel engine performance under its operating is critical for the prediction of malfunction development and subsequently functional failure detection. Analysis of instantaneous angular speed (IAS) of the crankshaft is considered as one of the nonintrusive and effective methods of the detection of combustion quality deterioration. In this paper results of experimental verification of fuel system's malfunction detecting, using optical encoder for IAS recording are presented. The implemented method relies on the comparison of measurement results, recorded under healthy and faulty conditions of the engine. Elaborated dynamic model of angular speed variations enables us to build templates of engine behavior. Recorded during experiment, values of cylinder pressure were taken for the approximation of pressure basic waveform. The main task of data processing is smoothing the raw angular speed signal. The noise is due to sensor mount vibrations, signal emitter machining, engine body vibrations, and crankshaft torsional vibrations. Smoothing of the measurement data was carried out by the implementation of the Savitzky-Golay filter. Measured signal after smoothing was compared with the model of IAS run.
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Johnston, Brian J., Alastair Ruffell, Patricia Warke, and Jennifer McKinley. "3DGPR for the Non-Destructive Monitoring of Subsurface Weathering of Sandstone Masonry." Heritage 2, no. 4 (2019): 2802–13. http://dx.doi.org/10.3390/heritage2040173.
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Remote sensing techniques, such as LiDAR and photogrammetry, are used by researchers exploring the spatial distribution of weathering features in historic masonry. These well-established tools provide users with a perspective of the processes affecting the surface of masonry blocks; however, they cannot provide information on the alteration occurring subsurface. Geophysical tools are being explored as a potential approach to observe the variation in material properties beneath masonry block surfaces and to examine the patterns of deterioration across wall sections. Applying such techniques inform the development of conceptual models of weathering at the block to building wall scale. In this study, ground-penetrating radar (GPR) was selected to inspect the subsurface condition of the wall section of an historic church wall, where areas of granular disintegration and flaking can be observed. 3DGPR was selected for this task, as its use of regular grids during data collection make it better suited for detecting features within an area. Three high-frequency antennas, 1.2 Ghz, 1.6 Ghz and 2.3 Ghz, were run across the study area in a series of 80 cm by 80 cm grids. The data were collated within GIS, where observed features were annotated onto a schematic of the wall surface. The 3DGPR outputs identified anomalies within this structure that could not have been as easily interpreted using a 2DGPR transect. However, as 3DGPR relies upon interpolative techniques to estimate the returns between observation transects, the validity of features detected in these locations need to be tested. The results of this application of 3DGPR identified variable weathering response across the wall section, relative to elevation. These observations were used to develop a conceptual model linking these findings to seasonal variation in the capillary rise of groundwater, upward from the base of the church wall. Through these findings it is possible to see how GPR can assist in developing our understanding of the processes threatening heritage buildings.
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González-Valero, Gabriel, Félix Zurita-Ortega, Ramón Chacón-Cuberos, and Pilar Puertas-Molero. "Analysis of Motivational Climate, Emotional Intelligence, and Healthy Habits in Physical Education Teachers of the Future Using Structural Equations." Sustainability 11, no. 13 (2019): 3740. http://dx.doi.org/10.3390/su11133740.
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This study developed a Structural Equation Model (SEM) in order to identify the associations between motivational climate, emotional intelligence, adherence to the Mediterranean Diet (MD), Physical Activity (PA), and some health indicators in a sample of future teachers. A non-experimental and cross-sectional study was carried in 775 university students (22.22 ± 3.76), using as main scales the Perceived Motivational Climate in Sport Questionnaire (PMCSQ-2), Emotional Intelligence Inventory adapted for the sport context, Physical Activity Questionnaire for Adolescents (PAQ-A), Mediterranean Diet Quality Index (KIDMED), 20 meter Shuttle Run Test (20mSRT), and percentage of lean and fat mass as calculated by the Tanita TBF300®® (Tanita-Corporation, Tokyo, Japan) electronic scale. The results showed a positive association between emotional intelligence and task climate, whereas the relationship with an ego climate was low. Ego-oriented climate was positively related to MD and high levels of PA. Emotional intelligence was directly related to higher levels of PA and the level of adherence to MD. PA was negatively associated with fat mass and positively with lean mass and VO2MAX. Task-oriented motivational climate showed a positive relation with the emotional intelligence of young people. Ego-oriented motivational climates were related to higher rates of physical-sport engagement and better diet quality.
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Gribbestad, Magnus, Muhammad Umair Hassan, Ibrahim A. Hameed, and Kelvin Sundli. "Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency." Entropy 23, no. 1 (2021): 83. http://dx.doi.org/10.3390/e23010083.
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Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.
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Gribbestad, Magnus, Muhammad Umair Hassan, Ibrahim A. Hameed, and Kelvin Sundli. "Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency." Entropy 23, no. 1 (2021): 83. http://dx.doi.org/10.3390/e23010083.
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Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.
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Liu, Bang, Xili Dai, Haigang Gong, et al. "Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model." Mobile Information Systems 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/5214067.
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In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.
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Vysochyna, Alina, Natalia Stoyanets, Grzegorz Mentel, and Tadeusz Olejarz. "Environmental Determinants of a Country’s Food Security in Short-Term and Long-Term Perspectives." Sustainability 12, no. 10 (2020): 4090. http://dx.doi.org/10.3390/su12104090.
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About 10% of the world population suffered from hunger in 2018. Thereby, the main objective of this research is the identification of environmental drivers and inhibitors of a country’s food security in the short and long run. The Food Security Index (FSI) was constructed from 19 indicators using Principal Component Analysis. Identification of the short- and long-run relationships between the FSI and environmental factors was realized with the pooled mean-group estimator for 28 post-socialistic countries for 2000–2016. Empirical research results showed that a country’s food security in the short run is affected by greenhouse gas emissions but boosted by the increase of renewable energy production. Reduction of carbon dioxide emissions, electrification of rural populations, access to clean fuels, renewable energy production, arable land, and forest area growth might be essential tasks in order to ensure countries’ food security in the long-run.
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Sargsyan, Laura, and Filipe Martins. "Evaluation of a new visualization and analytics solution for slow control data for large scale experiments." EPJ Web of Conferences 245 (2020): 07001. http://dx.doi.org/10.1051/epjconf/202024507001.
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Large experiments in high energy physics require efficient and scalable monitoring solutions to digest data of the detector control system. Plotting multiple graphs in the slow control system and extracting historical data for long time periods are resource intensive tasks. The proposed solution leverages the new virtualization, data analytics and visualization technologies such as InfluxDB time-series database for faster access large scale data, Grafana to visualize time-series data and an OpenShift container platform to automate build, deployment, and management of application. The monitoring service runs separately from the control system thus reduces a workload on the control system computing resources. As an example, a test version of the new monitoring was applied to the ATLAS Tile Calorimeter using the CERN Cloud Process as a Service platform. Many dashboards in Grafana have been created to monitor and analyse behaviour of the High Voltage distribution system. They visualize not only values measured by the control system, but also run information and analytics data (difference, deviation, etc.). The new monitoring with a feature-rich visualization, filtering possibilities and analytics tools allows to extend detector control and monitoring capabilities and can help experts working on large scale experiments.
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Cabezas-Ares, Alfredo, María Jesús Delgado-Rodríguez, and Sonia de Lucas-Santos. "The Dynamics of Cyclical Convergence and Decoupling in the Environmental Performance of Spanish Regions." Sustainability 12, no. 20 (2020): 8569. http://dx.doi.org/10.3390/su12208569.
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Spain is aiming at moving towards a low-carbon economy. The success of this task will depend on the capacities of Spanish regions to follow the national energy and climate strategy and decarbonize their economies. In this paper, we analyze the CO2 fluctuations and links with economic activity, throughout the period 1990 to 2018, using dynamic factor and recursive estimation models. We confirm the robustness of our results by testing the stability of the estimated parameters. Our findings show that the dynamics of the pattern of synchronization and decoupling of the regions are quite similar. Nonetheless, there are significant differences regarding the comparison of levels and trajectories across regions, emphasizing the need to intensify efforts to achieve national objectives. This study highlights interest in utilizing the proposed approach for the analysis of the short-run dynamics of environmental performance.
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Muñoz-Salinas, Rafael, Eugenio Aguirre, Miguel García-Silvente, and Moisés Gómez. "A multi-agent system architecture for mobile robot navigation based on fuzzy and visual behaviour." Robotica 23, no. 6 (2005): 689–99. http://dx.doi.org/10.1017/s0263574704001390.
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A multi-agent system based on behaviour for controlling the navigation task of a mobile robot in office-like environments is presented. The set of agents is structured into a three-layer hybrid architecture. A high level of abstraction plan is created using a topological map of the environment in the Deliberative layer. It is composed by the sequence of rooms and corridors to traverse and doors to cross in order to reach a desired room. The Execution and Monitoring layer translates the plan into a sequence of available skills in order to achieve the desired goal and monitors the execution of the plan. In the Control layer there is a set of agents that implements fuzzy and visual behaviours that run concurrently to guide the robot. Fuzzy behavior manages the vagueness and uncertainty of the range sensor information allowing to navigate safely in the environment. Visual behavior locates a required door to cross and fixate it, indicating the appropriate direction to reach it. Artificial landmarks are placed beside the doors to show its position. The system has been implemented in a Nomad 200 mobile robot and has been validated in numerous experiments in a real office-like environment.
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Jothiprakash, V., and R. Mohandoss. "A weekly operational planning model for a run-off-the-river system: some simulation results from the Tambiraparani river, Tamil Nadu, India." Water Policy 12, no. 3 (2009): 318–35. http://dx.doi.org/10.2166/wp.2009.094.
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In the present study, a weekly operational planning model has been developed using a simulation technique for the Tambiraparani river system in Tamil Nadu, India. A planning model has been developed incorporating the important features of the existing operational policy. Paddy is the main crop cultivated in three seasons, although banana is cultivated to an appreciable extent, it has not been recognized in the existing operational policy. The major task of the water resources manager is to find the area under paddy during the third season namely, the Advance Kar season. The main objective of the present study is to simulate the temporal distribution of the available water in the Tambiraparani river system. Simulation is carried out for various possible extents of Advance Kar area and banana area for various initial storage conditions. It is found that the entire Tambiraparani river system could be managed without deficit by reducing the release of water by 10–25% during the non-stress crop periods and also by lagging the starting period of the season by one or two weeks. If this management is practiced during average inflow years, it is possible to maintain the same final storage as the initial storage during the start of the season. Thus the polices derived using the present study would be more useful to the system managers enabling them to authorize the area under paddy during the Advance Kar season and also to help them to maintain the required storage for the next season.
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Angelakis, E., L. Fuhrmann, I. Myserlis, et al. "F-GAMMA: Multi-frequency radio monitoring ofFermiblazars." Astronomy & Astrophysics 626 (June 2019): A60. http://dx.doi.org/10.1051/0004-6361/201834363.
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Context. The advent of theFermigamma-ray space telescope with its superb sensitivity, energy range, and unprecedented capability to monitor the entire 4πsky within less than 2–3 h, introduced a new standard in time domain gamma-ray astronomy. Among several breakthroughs,Fermihas – for the first time – made it possible to investigate, with high cadence, the variability of the broadband spectral energy distribution (SED), especially for active galactic nuclei (AGN). This is necessary for understanding the emission and variability mechanisms in such systems. To explore this new avenue of extragalactic physics theFermi-GST AGN Multi-frequency Monitoring Alliance (F-GAMMA) programme undertook the task of conducting nearly monthly, broadband radio monitoring of selected blazars, which is the dominant population of the extragalactic gamma-ray sky, from January 2007 to January 2015. In this work we release all the multi-frequency light curves from 2.64 to 43 GHz and first order derivative data products after all necessary post-measurement corrections and quality checks.Aims. Along with the demanding task to provide the radio part of the broadband SED in monthly intervals, the F-GAMMA programme was also driven by a series of well-defined fundamental questions immediately relevant to blazar physics. On the basis of the monthly sampled radio SEDs, the F-GAMMA aimed at quantifying and understanding the possible multiband correlation and multi-frequency radio variability, spectral evolution and the associated emission, absorption and variability mechanisms. The location of the gamma-ray production site and the correspondence of structural evolution to radio variability have been among the fundamental aims of the programme. Finally, the programme sought to explore the characteristics and dynamics of the multi-frequency radio linear and circular polarisation.Methods. The F-GAMMA ran two main and tightly coordinated observing programmes. The Effelsberg 100 m telescope programme monitoring 2.64, 4.85, 8.35, 10.45, 14.6, 23.05, 32, and 43 GHz, and the IRAM 30 m telescope programme observing at 86.2, 142.3, and 228.9 GHz. The nominal cadence was one month for a total of roughly 60 blazars and targets of opportunity. In a less regular manner the F-GAMMA programme also ran an occasional monitoring with the APEX 12 m telescope at 345 GHz. We only present the Effelsberg dataset in this paper. The higher frequencies data are released elsewhere.Results. The current release includes 155 sources that have been observed at least once by the F-GAMMA programme. That is, the initial sample, the revised sample after the firstFermirelease, targets of opportunity, and sources observed in collaboration with a monitoring programme following up onPlancksatellite observations. For all these sources we release all the quality-checked Effelsberg multi-frequency light curves. The suite of post-measurement corrections and flagging and a thorough system diagnostic study and error analysis is discussed as an assessment of the data reliability. We also release data products such as flux density moments and spectral indices. The effective cadence after the quality flagging is around one radio SED every 1.3 months. The coherence of each radio SED is around 40 min.Conclusions. The released dataset includes more than 3 × 104measurements for some 155 sources over a broad range of frequencies from 2.64 GHz to 43 GHz obtained between 2007 and 2015. The median fractional error at the lowest frequencies (2.64–10.45 GHz) is below 2%. At the highest frequencies (14.6–43 GHz) with limiting factor of the atmospheric conditions, the errors range from 3% to 9%, respectively.