A Web-Services-Based P2P Computing-Power Sharing Architecture

As demands of data processing and computing power are increasing, existing information system architectures become insufficient. Some organizations try to figure out how to keep their systems work without purchasing new hardware and software. Therefore, a Webservices-based model which shares the resource over the network like a P2P network will be proposed to meet this requirement in this paper. In addition, this paper also discusses some problems about security, motivation, flexibility, compatibility and workflow management for the traditional P2P power sharing models. Our new computing architecture - Computing Power Services (CPS) - will aim to address these problems. For the shortcomings about flexibility, compatibility and workflow management, CPS utilizes Web Services and Business Process Execution Language (BPEL) to overcome them. Because CPS is assumed to run in a reliable network where peers trust each other, the concerns about security and motivation will be negated. In essence, CPS is a lightweight Web-Services-based P2P power sharing environment and suitable for executing computing works in batch in a reliable networ

Parameters to characterize the internal recirculation of an oxidation ditch

Mixed liquor circulates ceaselessly in the closed-loop corridor in an oxidation ditch (OD), which is significantly different from other wastewater treatment processes. The internal recirculation ratio (IRR), i.e., the ratio between circulation flow rate (QCC) and influent flow rate (QIn), and the circulatory period (T), i.e. the time consumed for the mixed liquor to complete one lap in the circular corridor, was used to quantify the internal recirculation characteristics of the OD system. In order to elucidate the characteristics and  applicability of IRR and T, this study obtained the numerical relationship between IRR and T by formula derivation. It also discusses the factors influencing IRR and analyses the applications of IRR and T. The results showed that IRR = QCC/QIn = HRT/T = HRT • IRF (HRT = hydraulic retention time of the mixed liquor in the circular corridor; IRF = internal recirculation frequency). Moreover, three kinds of parameters had an effect on IRR: QIn; reactor dimensions, i.e., length (Lmid), width (B), and height (H) of the circular corridor; and horizontal velocity of the mixed liquor in the circular corridor (v). QIn changed IRR by altering HRT. However, B, H, Lmid, and v changed IRR by altering IRF and T. Furthermore, the same IRR corresponded to many different HRT and IRF. Therefore, when QIn and QCC varied in the OD system, using HRT and IRF to evaluate the variation of QIn and QCC, respectively, was better than using IRR to evaluate their synthetical variation. IRF and T were useful for directly and precisely characterizing the circulation speed and circulation flow rate in the circular corridor, while IRR was more useful for evaluating the dilution effect of reflux on influent

Vehicle Path Planning with Multicloud Computation Services

With the development of artificial intelligence, public cloud service platforms have begun to provide common pretrained object recognition models for public use. In this study, a dynamic vehicle path-planning system is developed, which uses several general pretrained cloud models to detect obstacles and calculate the navigation area. The Euclidean distance and the inequality based on the detected marker box data are used for vehicle path planning. Experimental results show that the proposed method can effectively identify the driving area and plan a safe route. The proposed method integrates the bounding box information provided by multiple cloud object detection services to detect navigable areas and plan routes. The time required for cloud-based obstacle identification is 2 s per frame, and the time required for feasible area detection and action planning is 0.001 s per frame. In the experiments, the robot that uses the proposed navigation method can plan routes successfully

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1