Off-line Module Research Articles

Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network

The faults occurring in the photo voltaic system has to be detected to make it work efficiently .To detect and classify the faults occurring in the photo voltaic module infrared images, electro luminescent images, photo luminescent images of photo voltaic module is used .Using infrared images around 11 faults of photovoltaic module such as cell ,cell-multi, hot-spot-multi , hot-spot, cracking, diode, diode-multi, vegetation, shadowing, off-line module and soiling faults can be detected. In addition to the original infra-red images (IR) available in the IR dataset, the IR images are generated for each and every category of faults by using generative adversarial networks (GAN’s) to increase the dataset size. 45000 images are generated by GAN’s. Later the images are used to train and test the convolution neural network. The dataset visualization of original and that of GAN generated images are done in 2-dimensional space using uniform manifold approximation and projection. In this work 12 categories of IR dataset are considered for classification in which 11 belongs to fault category and the remaining one is the normal category of images. In earlier work only 11 category of faults or less than that is considered for classification. Compared the results with the existing work and it is found that by enhancing the dataset size by GAN’s accuracy of 91.7 % is obtained during the classification of 8 categories of faults.

Combining HF rTMS over the Left DLPFC with Concurrent Cognitive Activity for the Offline Modulation of Working Memory in Healthy Volunteers: A Proof-of-Concept Study.

It has been proposed that the effectiveness of non-invasive brain stimulation (NIBS) as a cognitive enhancement technique may be enhanced by combining the stimulation with concurrent cognitive activity. However, the benefits of such a combination in comparison to protocols without ongoing cognitive activity have not yet been studied. In the present study, we investigate the effects of fMRI-guided high-frequency repetitive transcranial magnetic stimulation (HF rTMS) over the left dorsolateral prefrontal cortex (DLPFC) on working memory (WM) in healthy volunteers, using an n-back task with spatial and verbal stimuli and a spatial span task. In two combined protocols (TMS + WM + (maintenance) and TMS + WM + (rest)) trains of stimuli were applied in the maintenance and rest periods of the modified Sternberg task, respectively. We compared them to HF rTMS without a cognitive load (TMS + WM −) and control stimulation (TMS − WM + (maintenance)). No serious adverse effects appeared in this study. Among all protocols, significant effects on WM were shown only for the TMS + WM − with oppositely directed influences of this protocol on storage and manipulation in spatial WM. Moreover, there was a significant difference between the effects of TMS + WM − and TMS + WM + (maintenance), suggesting that simultaneous cognitive activity does not necessarily lead to an increase in TMS effects.

Dynamic simulation of cross-ventilated buildings with night-flush cooling in neighbourhood environment using integrated CFD-CFD-BES strategy

Night flush cooling is an effective passive strategy to condition spaces and provide larger capacity to store heat. A common technique to quantify the benefit from this cooling strategy is using building energy simulation (BES) modelling, which, however, is weak in capturing the airflow patterns and convective heat transfer under the complex urban contexts. Therefore, dynamic coupling techniques of BES to computational fluid dynamics (CFD) are often used to improve the simulation performance of the night flush cooling strategies.Nonetheless, dynamic coupling techniques always encounter a high computational cost to achieve accurate CFD calculations for naturally ventilated scenarios, as high-resolution grids are required to be prepared. Therefore, a framework of integration of a fine-resolution CFD grid (CFDf), a coarse-resolution CFD grid (CFDc) and BES is proposed in this work to study night-flush cooling. In this framework, the CFDf domain performs as the off-line module that generates the boundary conditions for the CFDc domain at its openings before the start of the iterative calculations. CFDc and BES domains are then performing a fully dynamic external coupling to achieve a convergence for the energy simulation using the updated convective heat transfer coefficient (CHTC) profiles at the exterior surfaces. A case study is investigated for a simple urban morphology in Los Angeles on a typical hot day. The results highlight an improvement in representation of the neighbourhood effect in energy calculations obtained in a feasible calculation run-time.

Toward multi-lingual information retrieval system based on internet linguistic diversity measurement

Abstract We introduce a method for measuring the quantity of online content of a set of languages at domain level. This measurement is used for building a Multi-Lingual Information Retrieval (MLIR) system that identifies which languages are strongly represented on the internet about a specific query topic. The system architecture includes two modules; the off-line module builds a linguistic diversity index for languages at topic level and the on-line module, where the suitable language for search is identified based the index for retrieving the relevant documents to the user query in that language. The conducted experiments explore the usefulness of building such an index and its usage effect on both of monolingual and traditional MLIR system. From the obtained results, it has been proven that the more internet resources, the better the accuracy of the retrieved results, and therefore the better the system performance.

Improved Selective Harmonics Elimination Scheme With Online Harmonic Compensation for High-Power PWM Converters

To reduce the low-order harmonics produced by the high-power pulsewidth-modulated (PWM) converters, selective harmonic elimination (SHE) scheme is commonly used due to its superior harmonic performance at low switching frequency. However, as an off-line modulation technique, the SHE scheme itself lacks the capability to realize the active compensation of the grid background harmonics. To enable the active compensation ability of the SHE-modulated PWM converters, this paper proposes an active compensation method through the jittering of SHE phase angle. The proposed method can realize the real-time compensation of the preexisting system background harmonics in high-power PWM converters' system. An application example on a high-power PWM current-source rectifier (CSR) system is provided in this paper. Experimental results show that the proposed method can effectively attenuate the line current harmonics caused by the grid background harmonics in the high-power PWM CSR systems.

A reliable control system for measurement on film thickness in copper chemical mechanical planarization system

In recent years, a variety of film thickness measurement techniques for copper chemical mechanical planarization (CMP) are subsequently proposed. In this paper, the eddy-current technique is used. In the control system of the CMP tool developed in the State Key Laboratory of Tribology, there are in situ module and off-line module for measurement subsystem. The in situ module can get the thickness of copper film on wafer surface in real time, and accurately judge when the CMP process should stop. This is called end-point detection. The off-line module is used for multi-points measurement after CMP process, in order to know the thickness of remained copper film. The whole control system is structured with two levels, and the physical connection between the upper and the lower is achieved by the industrial Ethernet. The process flow includes calibration and measurement, and there are different algorithms for two modules. In the process of software development, C++ is chosen as the programming language, in combination with Qt OpenSource to design two modules' GUI and OPC technology to implement the communication between the two levels. In addition, the drawing function is developed relying on Matlab, enriching the software functions of the off-line module. The result shows that the control system is running stably after repeated tests and practical operations for a long time.

Exploring latent browsing graph for question answering recommendation

In this paper, we develop a framework of Question Answering Pages (referred to as QA pages) recommendation. Our proposed framework consists of the two modules: the off-line module to determine the importance of QA pages and the on-line module for on-line QA page recommendation. In the off-line module, we claim that the importance of QA pages could be discovered from user click streams. If the QA pages are of higher importance, many users will click and spend their time on these QA pages. Moreover, the relevant relationships among QA pages are captured by the browsing behavior on these QA pages. As such, we exploit user click streams to model the browsing behavior among QA pages as QA browsing graph structures. The importance of QA pages is derived from our proposed QA browsing graph structures. However, we observe that the QA browsing graph is sparse and that most of the QA pages do not link to other QA pages. This is referred to as a sparsity problem. To overcome this problem, we utilize the latent browsing relations among QA pages to build a QA Latent Browsing Graph. In light of QA latent browsing graph, the importance score of QA pages (referred to as Latent Browsing Rank) and the relevance score of QA pages (referred to as Latent Browsing Recommendation Rank) are proposed. These scores demonstrate the use of a QA latent browsing graph not only to determine the importance of QA pages but also to recommend QA pages. We conducted extensive empirical experiments on Yahoo! Asia Knowledge Plus to evaluate our proposed framework.

Model of an advance-booking system for highway trips

In the 16th International symposium on transportation and traffic theory, we had presented a novel demand management concept known as the Highway Space Inventory Control System (HSICS). The basic idea of HSICS is that all road users have to make reservations in advance to enter the highway. The system allows highway operators to make real-time decisions whether to accept or reject travellers’ requests to use the highway system in order to achieve certain system-wide objectives. The proposed HSICS model consists of two modules – Highway Allocation System (HAS) and the Highway Reservation System (HRS). The HAS is an off-line module that determines the maximum number of trips from each user class (categorized based on time of departure, vehicle type, vehicle occupancy, and trip distance) to be accepted given a pre-defined demand. It develops the optimal highway allocations for different traffic scenarios. The “traffic scenarios-optimal allocations” data obtained in this way enables the development of HRS. The HRS module operates in the on-line mode, where there is uncertainty in the travel demand, to determine whether a request to make a trip between certain origin–destination pair during a certain time interval is accepted or rejected. A shortcoming of the proposed HAS formulation [Teodorovic, D., Edara, P., 2005. Highway space inventory control system. In: Proceedings of the ISTTT 16: Transportation and Traffic Theory: Flow, Dynamics and Human Interaction, Elsevier Publishers, Maryland, USA, pp. 43–62] was the assumption of constant speed which would lead to less accurate position updates. In this paper, we replaced this constant average speed with link-specific space mean speed that is estimated using the Greenshields speed–density relationship. This led to a HAS formulation that is complex but more accurate than the previous one. The proposed model was illustrated using a numerical example of a highway section. Due to the non-linearity of the formulation, search for optimal solution was abandoned and a good solution was obtained using Genetic Algorithms together with penalty functions.

A LEARNING-BASED REAL-TIME CONTROLLER FOR DYNAMIC MANUFACTURING CELLS

A flexible controller (scheduling system) is essential if multiple performance objectives are to be met and uncertainty handled during production. In this paper, a learning-based, real-time scheduling system for controlling a manufacturing cell is presented. To develop the controller, an off-line module utilizes simulation to generate training samples to initialize the knowledge bases within the control system. During the operation, the difference between the performance level and the output from knowledge bases will be used to update and maintain the knowledge bases through reinforcement learning. To accomplish both off-line training and reinforcement learning, a CMAC (Cerebellar Model Articulation Controller) network is adopted to develop each knowledge base. The experimental results from simulation show that the controller performs well under multiple criterion environments. The simulation results showed improvement in system performance when reinforcement learning is incorporated in the feedback loop.

A LEARNING-BASED REAL-TIME CONTROLLER FOR DYNAMIC MANUFACTURING CELLS

ABSTRACT A flexible controller (scheduling system) is essential if multiple performance objectives are to be met and uncertainty handled during production. In this paper, a learning-based, real-time scheduling system for controlling a manufacturing cell is presented. To develop the controller, an off-line module utilizes simulation to generate training samples to initialize the knowledge bases within the control system. During the operation, the difference between the performance level and the output from knowledge bases will be used to update and maintain the knowledge bases through reinforcement learning. To accomplish both off-line training and reinforcement learning, a CMAC (Cerebellar Model Articulation Controller) network is adopted to develop each knowledge base. The experimental results from simulation show that the controller performs well under multiple criterion environments. The simulation results showed improvement in system performance when reinforcement learning is incorporated in the feedback loop.

Intelligent scheduling and control of rail-guided vehicles and load/unload operations in a flexible manufacturing system

This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS). A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS). In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes. These data can be collected using a data acquisition device and stored in a dynamic database. The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies. At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected. The data are then structured into training data pairs to properly train an artificial neural network. The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations. At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network. If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network. The recommended control strategies will be transmitted to the MHS control for real-time execution. If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities. The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control. Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates.

Optimal Pulsewidth Modulation for Current Source Inverters

This paper describes an off-line pulsewidth modulation (PWM) technique intended for the reduction of detrimental current harmonics in current source inverter systems. The procedure is based on the minimization of the amplitudes of offending harmonics through the determination of optimal-pulse positions using a gradient primal-dual algorithm.