Abstract: Active electromagnetic isolator systems are used to isolate vibration with variable stiffness and damping, flexible controllability, varied control methods and fast response. In order to achieve better vibration isolation performance, an electromagnetic isolator system is designed with small volume and simple structure and the control block diagram with the nonlinear relationships and PID controller is proposed to simulate and verify in this paper. The structure of the electromagnetic isolator system can not only save space, but also provide the controllable electromagnetic force to counteract external disturbances quickly and actively. For improving the vibration isolation performance of the designed electromagnetic isolator…system, a mathematical model which can accurately reflect the relationship between electromagnetic force and coil current and gap is established using structural information and experimental data. A control block diagram of the electromagnetic vibration isolator system containing nonlinear characteristics with PID controller is then designed. The PID control system shows the good performance of active anti-jamming of the designed isolator system. The experimental result demonstrates that the designed electromagnetic isolator system can effectively isolate vibration, and that the control block diagram with PID controller based on the nonlinear system can effectively describe and control the electromagnetic isolator system. The better vibration isolation performance, such as fast response, less overshoot and so on, can be achieved through deliberately designed controllers.
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Keywords: Electromagnetic isolator system, vibration control, PID controller, nonlinear system
Abstract: Machine learning based intelligent diagnosis methods can adaptively generate the fault diagnosis model by historical data, which have attracted much attention. Artificial neural network (ANN) is one of the most important tools for gearbox intelligent diagnosis. However, the training of ANN has the problem of local optima, and it is hard to determine the ANN structure. These problems have great influence on the diagnosis performance of ANN. In this paper, a variable neural network (RegPSOVNN) is proposed for gearbox fault diagnosis based on regrouping particle swarm optimization. Ten time-domain features are selected to form the input of the ANN. Regrouping…particle swarm optimization (RegPSO) is utilized for the optimization of ANN structure and network training. It can simultaneously optimize the structure and parameters of ANN and effectively avoid the problem of local optima. To evaluate the diagnosis performance of the proposed method, gearbox failure experiments were conducted, and backpropagation neural network (BPNN), firefly variable neural network (FAVNN) and particle swarm optimization based variable neural network (PSOVNN) were used for comparison. Experimental results indicated that the proposed method can effectively optimize the network structure and diagnosis the gearbox faults.
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Abstract: The pathogenesis of Alzheimer’s disease (AD) is complicated and involves multiple contributing factors. Mounting evidence supports the concept that AD is an age-related metabolic neurodegenerative disease mediated in part by brain insulin resistance, and sharing similar metabolic dysfunctions and brain pathological characteristics that occur in type 2 diabetes mellitus (T2DM) and other insulin resistance disorders. Brain insulin signal pathway is a major regulator of branched-chain amino acid (BCAA) metabolism. In the past several years, impaired BCAA metabolism has been described in several insulin resistant states such as obesity, T2DM and cardiovascular disease. Disrupted BCAA metabolism leading to elevation in circulating…BCAAs and related metabolites is an early metabolic phenotype of insulin resistance and correlated with future onset of T2DM. Brain is a major site for BCAA metabolism. BCAAs play pivotal roles in normal brain function, especially in signal transduction, nitrogen homeostasis, and neurotransmitter cycling. Evidence from animal models and patients support the involvement of BCAA dysmetabolism in neurodegenerative diseases including Huntington’s disease, Parkinson’s disease, and maple syrup urine disease. More recently, growing studies have revealed altered BCAA metabolism in AD, but the relationship between them is poorly understood. This review is focused on the recent findings regarding BCAA metabolism and its role in AD. Moreover, we will explore how impaired BCAA metabolism influences brain function and participates in the pathogenesis of AD.
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Abstract: Data owners are expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be to an acceptable level. Typically, a disclosure algorithm evaluates potential generalization functions in a predetermined order, and then discloses the first generalization that satisfies the desired privacy property. Recent studies show that adversarial inferences using knowledge about such disclosure algorithms can usually render the algorithm unsafe. In this paper, we show…that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k -jump algorithms. We then prove that the data utility of different k -jump algorithms is generally incomparable. The comparison of data utility is independent of utility measures and syntactic privacy models. Finally, we analyze the computational complexity of k -jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.
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Abstract: PURPOSE: To describe recurrent admissions in a cohort of complex chronic patients at a specialty children's hospital, identify factors that contribute to multiple admissions, and test the hypothesis that risk factors predict patterns of readmissions within specified time intervals. METHODS: Retrospective cohort analysis of patients admitted to a specialty children's hospital during calendar year 2006 followed through 2011. Administrative and medical record abstracted data were analyzed by the total number of recurrent admissions and by readmissions with 7, 30 and 90 days at any point during the five year study period. RESULTS: One thousand two hundred and twenty-nine patients with…2295 inpatient admissions were examined. %467 Four hundred and sixty-seven patients (38%) experienced at least one additional inpatient admission at any time during the study period. Eight variables were significant risk factors for subsequent admission at any time during the study period: indwelling technology, mobility support, critical care consultation, medical (vs. surgical) admission, mean LOS across all admissions, number of scheduled medications at discharge, insurance on index admission, and gross charges on index admission. Presence of indwelling technology, increasing numbers of scheduled medications at discharge and Nervous System APR-DRG diagnoses were significant factors predicting readmission within 7, 30, and 90 day intervals. CONCLUSIONS : Within this population of complex chronic patients risk factors were identified that predict vulnerability to recurrent admissions suggesting that further research is needed to address a unique subset of complex chronic patients and the complement of systems organized to provide health care delivery services for them.
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Keywords: Recurrent admissions, complex chronic condition, readmission, children with disabilities, specialty hospitals, risk factors, predictors
Abstract: Landmark based heuristics are among the most accurate current known admissible heuristics for cost optimal planning. A disjunctive action landmark can be seen as a form of at-least-one constraint on the actions it contains. In many domains, there are many critical propositions which have to be established for a number of times. Previous landmarks are too weak to express this kind of general cardinality constraints. In this paper, we propose to generalize landmarks to multi-valued landmarks to model general cardinality constraints in cost optimal planning. We show existence of complete multi-valued landmark sets by explicitly constructing complete multi-valued action landmark…sets for general planning tasks. Because exact lower bounds of general multi-valued action landmarks are intractable to extract and exploit, we introduce multi-valued proposition landmarks from which multi-valued action landmarks can be efficiently induced. Finally, we devise a linear programming based multi-valued landmark heuristic h lpml which extracts and exploits multi-valued landmarks using a linear programming solver. h lpml is guaranteed to be admissible and can be computed in polynomial time. Experimental evaluation on benchmark domains shows h lpml beats state-of-the-art admissible heuristic in terms of heuristic accuracy and achieves better overall coverage performance at the cost of using more CPU time.
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Keywords: Heuristic search based planning, landmark heuristic, cost optimal planning
Abstract: Solenoid valves (SVs) are used as actuators in various applications, which are crucial parts in control system. Their failure may result in a system crash, so its health condition and reliability are related to the safety of an engineering system. In order to explore the basis of condition-based maintenance or replacement of SVs, it is necessary to develop a prognostic approach to predict its operation condition and remaining useful life (RUL). In this paper, a particle filter (PF) technique, an online tracking method, is proposed to make prognostics for SV. Moreover, the Brownian motion degradation model is proposed, and the…distortion of dynamic driven current curve is assumed as the indicator of degradation state. To validate the proposed PF-based prognostics method, a degradation experiment is designed. The result shows that the predicted degradation state accurately reflects the real time filtered degradation state of SV and a good RUL prediction can be calculated by this method.
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Abstract: Homomorphic encryption always allows the linear arithmetic operations performed over the ciphertext and then returns equaling results as if the operations are taken over the original plaintext, which is always used for data aggregation in wireless sensor networks to keep the confidentiality of the data and cut down the transmission overhead of the ciphertext. In the marine sensor networks, sensors collect the multiple data such as temperature, salinity, pressure, and chlorophyll concentration in the ocean using a single hardware unit for further statistical analysis such as computing the mean and the variance and making regression analysis. However, directly using the…homomorphic encryption cannot perform well in marine sensor data forwarding since the data need to turn to satellites or vessels as relays and be forwarded in multi-hop way. The data are not expected to be decrypted until arriving the final destinations. To tackle these issues, we design a secure data forwarding protocol based on the Paillier homomorphic encryption and multi-use proxy re-encryption. We also evaluate the computational overhead in term of the delay in the transmission and operation in various test beds. The experiment results show that the additional computational overhead brought by cryptographic operations could be minor and it has the merit of providing fixed data size passing through the multi-hop transmission.
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Abstract: Precise precipitation forecast can better reflect the changing trend of climate, provide timely and efficient environmental information for management decision, as well as help people to make preparations for the incoming floods or droughts. However, existing approaches have limited ability to forecast future precipitation in different regions. In order to addess the problem, this paper proposes a big data based approach for precipitation forecasting based on deep belief nets, called DBNPF (Deep Belief Network for Precipitation Forecast). The proposed approach can not only learn the hierarchical representation of raw data using a highly generalized way, but also make a more…accurate description of the rule underlying different kind of environmental factors. A set of dedicated experiments with hydrological multivariate time series from four typical areas of China is conducted to validate the feasibility and robustness of the model. In the experiments, environmental factors, filtered by factor analysis, are used as input vector, and the next 24 hours precipitation is used as the output vector. We compare DBNPF with other traditional machine learning approaches. The experimental results show that the proposed approach is more robust than other approaches and can also improve the forecast precision.
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Keywords: Precipitation forecast, hydrological multivariate time series, data mining, deep-learnin2017 MSC: 02-11, 99-00