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Time-series 1-D signals are ubiquitous in industrial applications for monitoring and control. However, it is lacking of efficient tools to deal with simultaneously multiple time-series 1-D signals. To this end, in this article, a novel theory of image formation is proposed that converts multiple 1-D signals to 2-D images and takes advantages of convolutional neural network for feature extraction and classification of a sequence of images. A case study is carried out for the classification of working conditions in photovoltaic power systems. In total, 23 1-D signals are mapped to a sequence of 2-D images to derive six different models through image formation-based deep learning. They are tested through the outdoor experiments under time varying working conditions. We discover that physical implication in 2-D images affects significantly the classification performance such that 2-D images with the clustered currents or voltages tend to create better results while randomly arranged image patterns are prone to generate worse results. Excellent performance with an accuracy 96.09% is guaranteed when physical advantages are incorporated in the proposed tools. Driven by the deep learning approaches, the proposed tools are promising for complicated industrial applications.

The cutting-edge monitoring technique of photovoltaic power systems tends to employ cloud servers for big data analysis in the wake of smart sensors and internet of things (IoT). Various mismatch phenomena of photovoltaic arrays are generated due to the external and internal interactions, which would be identified by an appropriate monitoring approach. However, high dimensional sequential data provided by multiple sensors challenges the existing monitoring technologies. This paper proposes a dimension reduction technology mapping multiple sequence signals to a sequence of images which are processed further by a convolutional neural network (CNN), resulting in a novel condition monitoring system for photovoltaic array systems. Firstly, multiple sources of 1-dimensional time-series data are rearranged to construct 2-dimensional time-series images. Then, the CNN algorithm automatically extracts the underlying graphical features from data of 2-dimensional images for condition monitoring. Experiments were carried out upon self-made solar power stations to verify the effectiveness of the proposed method for real-world solar power systems. It shows that the data driven approach could identify effectively key operation conditions from the historical data with a negligible loss of features at the presence of mismatched phenomena.

To Integrate enterprise resource and solve the problems which were exposured in asset inventory of mobile base station, such as inefficiency and high error ratio in practice, this paper analyzed the feasibility of applying internet of things technology in asset inventory and established the system architecture and each function module of Base-station Asset Inventory System (BSAIS). By using the RFID and Bar-code technology, BSAIS realized the intelligent input and quasi real-time update of management information, improved the efficiency of inventory and reduced the human errors significantly.

This paper presented the design and implementation solution of virtual simulation experiment software in .NET framework environment. An OPC client was realized by C# programming language in the program, and communicated with the OPC server based on COM. By using LINQ technology to access SQL server database, simulation controlled objects library was built. Communication between C# application and Flash simulation object was also researched in this work. The experimental result shows that the virtual simulation experiment software is convenient to experiment management, data access, improve the quality of PLC experiment and reduce cost at the same time.

According to the logistics cost-effectiveness model, this paper points out that collaborative logistics has a performance advantage in cost savings, revenue increase, uncertainty reduce and revenue sharing, etc. On base of discussing the factors affecting the efficiency balance of collaborative logistics, coordination game theory suggests that logistics nodes will choose efficiency balance at different times or different tasks simultaneously or in stagger. As a result, synergy balance of logistics efficiency is achieved in the whole process or the overall.

Parallel computation is an effective technology to improve the executive performance of computer programs. In this paper, a new machining region parallel planning method is presented to generate optimal tool paths for 5-axis sculptured surface machining. Based on existing machining region planning methods, the improved method adopted data decomposition mode and OpenMP to implement the parallel computing strategy. By dividing the part surface into two or more areas, this method can generate tool paths with higher performance and shorter total length. This means it can reduce the cost of 5-axis machining. Computer implementation and examples were shown in this paper to prove the validity of the new method.

A new method based on level-set methods for tool path generation in die-cavities machining is represented in this paper, which can resolve most topological problems in die-cavities pocket milling, and can enhance the efficiency and quality of the machining. For the tool paths are sets of offset curves between the cavities outline and the internal islands, this approach adopted the principle of level-set methods and treated with tool paths in one milling layer as shape-varying wave front curves. This method translated the problem how to generate offset tool paths into a problem to solve the expression of zero sets in different time of a 3D surface, which is called as level-set function. Compare with other conventional methods, this method overcame the shortages such as path singularities, self-interactions and complexity of implement ways, and generated tool paths naturally and stably by its inherent feature inherited from the level-set Methods. The illustrative example of compute implementation in this paper has proved the effectivity and succinctness of this method sufficiently. Therefore, as a feasible way, this method is suitable for generating tool paths for die-cavities NC machining.