Feature extraction and health status prediction in PV systems

Diagnosis aims at predicting the health status of components and systems. In photovoltaic systems, it is vital to guarantee energy production and extend the useful life of photovoltaic power plants. Multiple prediction and classification algorithms have been proposed for this purpose in the literature. The accuracy of these algorithms depends directly on the quality of the data and the features with which they are tuned or trained. In this paper, an innovative approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage. This approach first discriminates severely affected photovoltaic panels using basic electrical features. In a second step, it discriminates the other faulty panels using more elaborated time–frequency features and selecting the most relevant features through correlation and variance analysis. Finally, the approach predicts the health status of photovoltaic panels using a nonlinear regression method named partial least squares. This later is then combined with linear discriminant analysis and compared. The approach is validated with real current data from a photovoltaic plant composed of twelve photovoltaic panels with power between 205 and 240 Wp in three health states, namely broken glass, healthy, and big snail trails. The results obtained show that the proposed approach efficiently predicts the three health states. It determines the level of degradation of the panels, which indicates priorities to corrective and predictive maintenance actions. Furthermore, it is cost-effective since it uses only electrical measurements that are already available in standard photovoltaic data acquisition systems. Above all, the approach is generic and it can be easily extrapolated to other diagnosis problems in other domains.     

An automatic energy saving strategy for a water dispenser based on user behavior

Nowadays, numerous public buildings provide water dispensers to supply drinking water which causes more energy consumption. A typical water dispenser periodically heats and cools the water to ensure that hot, warm, and cold water are always available for the user. However, this mechanism is inefficient because the users do not request hot and cool water continuously. Ideally, the boiling and cooling schedule should follow the demand pattern to save electricity consumption. When no demand, a water dispenser can enter a sleep mode.Therefore, this study presents an automatic energy-saving strategy for a water dispenser based on user behavior. The proposed system allows the water dispenser to automatically determine the appropriate time to heat, boil, and enter sleep mode based on user behavior. The proposed control strategy involves several steps. First, it collects historical data, analyzes water consumption behavior. The sensors installed in the water dispenser collect water consumption data. Second, this study applies Recurrent Neural Networks with Long-Short Term Memory to predict future water consumption. Finally, the proposed system utilizes the prediction result to determine heating, cooling, and sleep mode schedule.This study uses a water dispenser on a university campus as a prototype to test the proposed system. The effectiveness of the proposed system is measured by two factors, namely electricity consumption, and customer satisfaction. These two parameters are chosen because the proposed system should reduce electricity consumption while maintaining hot and cold water availability whenever needed. According to the simulation results, the proposed controlling strategy can reduce electricity consumption up to 28% monthly while maintaining a service level of 97%. This result shows that the proposed system is a good control system for water dispensers. By applying this controlling system, public buildings could reduce their energy bills without sacrificing their provision of drinking water.     

Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach

Prediction of vertical stress transmission in real soil profile using adaptive neuro-fuzzy inference system (ANFIS) is documented in this investigation. A soil bin facility holding a single-wheel tester was utilized to arrange controlled condition for exploration of the effects of wheel load, forward velocity, slippage and depth each at three different levels. A profile housing seven load cells was buried at different depths when data were transmitted to a data acquisitioning system for derivation of 81 data points and then to build ANFIS-based model. The Sugeno-type fuzzy rules were constituted with various membership functions in the representations. In the Sugeno-type fuzzy inference approach, the modal was developed according to the four input parameters. Performance evaluation criteria (i.e. MSE, MRE and R²) were incorporated in the study to find the highest quality solution. It was deduced, on the basis of performance criteria, that a Guassian membership function outperformed other tested membership functions. The results could serve as a catalyst to expedite the investigations in the realm of artificial intelligence application in prediction of soil stress transmission created by wheeled vehicle trafficking.     

新型口服抗凝药在肿瘤相关性血栓栓塞的研究进展

静脉血栓栓塞症（VTE）是肿瘤常见的并发症之一，也是肿瘤患者的第二大死因。虽然低分子肝素（LMWH）是治疗肿瘤相关VTE的首选方案，但是长期注射的不适与过高的费用，导致病人很难长期维持。新型口服抗凝药（NOACs）因其口服方便，相比华法林受食物药物影响小，且无需常规监测等优点而受到广泛关注。最近比较NOACs与LMWH治疗肿瘤相关性VTE的研究表明，NOACs在肿瘤相关性血栓（CAT）的疗效方面，不劣于LMWH，但有增加出血的风险。在某些肿瘤中，它们可能是LMWH合理的替代方案。本文总结了NOACs用于治疗和预防CAT的已完成或尚在进行的临床研究，后续研究将提供更多的证据指导NOACs应用。     

Towards an efficient SEU effects emulation on SRAM-based FPGAs

A novel fault injection approach, reproducing results obtained from radiation ground testing while studying the Single Event Upset (SEU) effects on SRAM-based Field Programmable Gate Arrays (FPGAs), is presented. This approach can take into account the relative sensitivity difference between configuration bits set to ‘0’ and those set to ‘1’. According to irradiation experiments conducted under proton beam for a Xilinx Virtex-5 FPGA at the TRIUMF lab, configuration bits set to ‘1’ are approximately twice as sensitive as bits set to ‘0’. This fact was exploited in test sequence generation while performing fault injection experiments, in order to generate more realistic emulation results. The effectiveness of the approach is validated by comparing its results to those obtained with proton radiation tests, for two different ring-oscillator-based experimental setups. It shows that taking this sensitivity into account helps obtain more realistic results while dealing with delays induced by radiation, which justifies considering this relative sensitivity during fault emulation. In fact, comparing the results obtained from the proposed approach to those obtained at TRIUMF gives an absolute relative error of 3.1 and 14%, respectively, for the first and the second setups, while estimating the error between the latter and results from a conventional random fault injection provides error values of up to 75%. Finally, applying our fault injection approach on a more conventional circuit reveals that taking the relative sensitivity difference into account leads to 2.3 times as many errors detected as with random injection. This last result suggests that not taking the relative sensitivity difference into account during emulation can lead to an underestimation of a design sensitivity to radiation.     

Structural analysis based sensor measurement fault diagnosis in cement industries

This investigation presents a fault diagnosis methodology for detecting sensor faults in cement industries pyro processing section. It works in three steps: (a) modelling, (b) analysis, and (c) validation. In the modelling, the actual data from the cement pyro processing is used to do a correlation analysis between output and input variables. The structural model is obtained from the correlation tests. During the analysis phase the Structural analysis Tool (SaTool) is used to detect the detectability and isolability of the faults. The results of the structural analysis are validated in a cement industry using residual analysis performed using structural sensor model and real-time measurements. The main advantages of this fault diagnosis technique are: (a) it requires only correlation analysis to obtain the structural model without a detailed physical model as in other methods, (b) conclusions regarding detectability and isolability can be easily drawn during the analysis stage itself, and (c) the method is simple compared to the model-based, and data-history based methods. The effectiveness of the proposed method is illustrated using data from cement pyro processing plant and its performance is compared with model based approaches for four different types of sensor faults: (1) bias, (2) drift, (3) stuck, and (4) measurement failures. Our results demonstrate that the structural method is able to detect the sensor faults even in the presence of noisy information, and its performance is comparable with that of model based approaches without employing a physical model.     

Improving the UV resistance of aromatic poly(1,3,4‐oxadiazole) fiber using the disperse dye modified with octavinyl POSS. Part 2: Fixing form of dye and UV resistance ability

Disperse Red 60 modified with octavinyl polyhedral oligomeric silsesquioxanes (POSS) was used to color poly(p‐phenylene‐1,3,4‐oxadiazoles) (p‐POD) fiber. The dyed p‐POD fiber was given high UV resistance and excellent color strength (K/S value) simultaneously. And the effects of the UV irradiation time on the K/S values and the strength retention of dyed fiber were investigated. The results indicated that the modified dye (1:3) molecules can easily be adsorbed on fiber surface and diffuse into fiber, whose fixing form on the fiber surface is a semiembedded type. Meanwhile, the surface morphology, the aggregation structure, the thermal property, the color fastness, and anti‐UV durability of the dyed p‐POD fiber were characterized. After UV accelerated aging, the macroscopic and microscopic structures of the dyed p‐POD fiber can well be maintained and the color fastness is also satisfactory. Meanwhile, this method also solves the problem of poor anti‐UV durability of the modified fiber with the previous processes. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44928.     

Effect of downward depth and inflation pressure on contact force of gasbag polishing

Gasbag polishing is a kind of ultra-precision machining technology by means of flexible contact, while how to control the polishing contact force online is one of the key issues. In this paper, by analyzing the effect of downward depth and inflation pressure on the contact force experimentally, and then the coupling contact force model is developed. Thus, the predictions of polishing contact force and inflation pressure in terms of the nonlinear composite material of rubber gasbag can be obtained, which can be used to get the optional combination and controllable range of polishing contact force, and to construct the control system of coupling contact force as well. Experimental study shows that applying coupling contact force model to the control system of gasbag polishing contact force with BP neural network PID control strategy is a proper method, which realizes the polishing contact force controllable online and uniform surface quality of mold.     

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.     

Towards a unifying domain model of construction safety,health and well-being: SafeConDM

Specific occupational construction safety, health, and well-being related knowledge and information are scattered and fragmented. Despite technological advancements of information and knowledge management, a link between safety management and information models is still missing. In this paper we present first steps towards a unifying formal (logic-based) domain model of construction safety, called SafeConDM, that consists of: (1) a semantically rich ontology of hazard, safety concepts, and concept relationships that builds on, and integrates with, existing construction safety ontologies and building information models; (2) a set of first-order if-then rules linking construction site states with the potential for specific hazards to occur that we define in a novel way using spatial artefacts. We present a prototype software tool, based on our ASP4BIM tool that implements SafeConDM for construction hazard analysis and safe construction planning decision support, and empirically evaluate our tool on three real-world construction building models.