RFUN is a small first-order reversible functional language introduced by Yokoyama et al. in 2012. the present paper aims to further the understanding of reversible functionalprogramming (and RFUN in particular) by de...
详细信息
In data-intensive real-time applications, e.g., transportation management and location-based services, the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information,...
详细信息
ISBN:
(纸本)9781479989379
In data-intensive real-time applications, e.g., transportation management and location-based services, the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., fast driving routes, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. To address the problem, we design a new real-time big data management framework, which supports a non-preemptive periodic task model for continuous in-memory sensor data analysis and a schedulability test based on the EDF (Earliest Deadline First) algorithm to derive information from current sensor data in real-time by extending the map-reduce model originated in functionalprogramming. As a proof-of-concept case study, a prototype system is implemented. In the performance evaluation, it is empirically shown that all deadlines can be met for the tested sensor data analysis benchmarks.
In data-intensive real-time applications, e.g., transportation management and location-based services, the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information,...
详细信息
ISBN:
(纸本)9781479989386
In data-intensive real-time applications, e.g., transportation management and location-based services, the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., fast driving routes, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. To address the problem, we design a new real-time big data management framework, which supports a non-preemptive periodic task model for continuous in-memory sensor data analysis and a schedulability test based on the EDF (Earliest Deadline First) algorithm to derive information from current sensor data in real-time by extending the map-reduce model originated in functionalprogramming. As a proof-of-concept case study, a prototype system is implemented. In the performance evaluation, it is empirically shown that all deadlines can be met for the tested sensor data analysis benchmarks.
this paper considers real-time cluster-based wireless sensor networks where the nodes harvest energy from the environment. We target performance sensitive applications that have to collectively send their information ...
详细信息
this paper considers real-time cluster-based wireless sensor networks where the nodes harvest energy from the environment. We target performance sensitive applications that have to collectively send their information to cluster head by a predefined deadline, such as in distributed real-time monitoring and detection. the nodes are equipped with Dynamic Modulation Scaling (DMS) capable wireless radios. the problem is to determine the time slots and modulation levels that will be used by each node while communicating withthe cluster-head in order to achieve energy-neutral (perpetual) operation and maximize energy reserves. We propose a solution that adjusts underlying TDMA slots that enables high energy nodes to compensate by transmitting faster producing larger slack for dark nodes, while meeting the performance constraint. We present an optimal mixed integer linear programming based solution. We also develop fast heuristics that are shown to provide approximate solutions through comprehensive experiments with actual solar energy harvesting profiles.
the Programmable logic Controller(PLC)-based Plant Protection System(PPS) in Nuclear Power Plants has the safety concerns,such as Common Cause Failure(CCF),complexity,and surveillance test *** analyzing the design con...
详细信息
the Programmable logic Controller(PLC)-based Plant Protection System(PPS) in Nuclear Power Plants has the safety concerns,such as Common Cause Failure(CCF),complexity,and surveillance test *** analyzing the design concept of the PPS in the perspective of CCF and complexity,this work describes the strategy based on diversity and defense in depth analyses to minimize the system complexity through structural and functional optimization for enhancing the *** diversity strategy involves separation of non-safety logics from PPS logics,adaptation of one-through channel test concept,and applying channel bypass *** are essential to implement simpler and easier hardware platform for both designers and operators to enhance the safety,reliability and economics compared to the PLC-based I&C safety system.
We develop yet another typed multi-stage calculus. lambda((sic)%). It extends Tsukada and Igarashi's. lambda((sic)) with cross-stage persistence and is equipped with all the key features that MetaOCaml-style multi...
详细信息
ISBN:
(纸本)9783319071510;9783319071503
We develop yet another typed multi-stage calculus. lambda((sic)%). It extends Tsukada and Igarashi's. lambda((sic)) with cross-stage persistence and is equipped with all the key features that MetaOCaml-style multi-stage programming supports. It has an arguably simple, substitution-based full-reduction semantics and enjoys basic properties of subject reduction, confluence, and strong normalization. Progress also holds under an alternative semantics that takes staging into account and models program execution. the type system of lambda((sic)%) gives a sufficient condition when residual programs can be safely generated, making. lambda((sic)%) more suitable for writing generating extensions than previous multi-stage calculi.
FCCM based on K-L information regularization is an FCM-type co-clustering model, which is a fuzzy counterpart of the probabilistic Multinomial Mixture Models (MMMs). In MMMs and other FCM-type co-clustering models, wh...
详细信息
FCCM based on K-L information regularization is an FCM-type co-clustering model, which is a fuzzy counterpart of the probabilistic Multinomial Mixture Models (MMMs). In MMMs and other FCM-type co-clustering models, whose goal is to simultaneously partition objects and items considering their mutual cooccurrence information, memberships of objects are forced to be exclusive in a similar way to FCM while item-memberships only represent the relative typicality in each cluster and are not forced to be exclusive. In this paper, a new co-clustering model is proposed by introducing the penalty for avoiding cluster overlapping in sequential fuzzy cluster extraction, which brings exclusive partition of items.
A hand bones radiograph is the gold standard for Rheumatoid Arthritis (RA) diagnosis. RA is an inflammatory disease that attacks the joint cartilage which causes premature mortality, disability, and it compromises the...
详细信息
A hand bones radiograph is the gold standard for Rheumatoid Arthritis (RA) diagnosis. RA is an inflammatory disease that attacks the joint cartilage which causes premature mortality, disability, and it compromises the quality of life. Early diagnosis and treatment of RA can carefully delay joint destruction, disease activity, and functional disability. To diagnose RA, the hand bones radiograph is to be taken and analyzed. Before the hand bones radiograph is analyzed, the first step is that the hand bones radiograph is carefully segmented. However the hand bones radiograph segmentation is an extremely exhausting and time consuming task for radiologists, not only because the hand radiograph has low quality and uneven illumination, but also because it is very complex. the precise segmentation is required during RA diagnosis. therefore an automatic segmentation method of bones is required. To correct the illumination and enhance the hand bones radiograph, we will employ a new morphology operation that is combined with a set of image processing. After correcting the illumination and enhancing the hand bones radiograph, the hand bones radiograph is segmented by applying fractal analysis. After the experiments on particular sets of hand radiograph images, we found that the proposed method works better compared withthe conventional segmentation methods used in our previous works.
this paper investigates the integration of linear constraints with MSVL. To this end, we first define linear constraint statements and discuss related issues of the incorporation. Further, for calling SMT solvers to s...
详细信息
ISBN:
(纸本)9780769550534
this paper investigates the integration of linear constraints with MSVL. To this end, we first define linear constraint statements and discuss related issues of the incorporation. Further, for calling SMT solvers to solve the newly introduced constraints, we give a translation algorithm from state programs in MSVL with linear constraints to SMT-LIB2.0 script language and then supply a solving procedure.
暂无评论