This paper introduces a new unit commitment problem, adapting extended priority list (EPL) method. The EPL method consists of two steps, in the first step we get rapidly some initial unit commitment problem schedules by priority list (PL) method. At this step, operational constraints are disregarded. In the second step unit schedule is modified using the problem specific heuristics to fulfill operational constraints. To calculate efficiently, however, note that some heuristics is applied only to solutions can expect improvement. Several numerical examples demonstrate the effectiveness of proposed method.

A fast technique for unit commitment problem by extended priority list

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this paper, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely, the Internet of Things, robotics, Artificial Intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This paper aims to open up new research opportunities for readers, particularly industrial practitioners.

IEEE transactions on industrial informatics

Forecasting short-term cycle time (CT) of wafer lots is crucial for production planning and control in the wafer manufacturing. A novel recurrent neural network called “bilateral long short-term memory (bilateral LSTM)” is proposed to model a short-term cycle time forecasting (CTF) of each re-entrant period of a wafer lot. First, a two-dimensional (2-D) architecture is designed to transmit the wafer and layer correlations by using wafer and layer connections. Subsequently, aiming to store various error signals caused by the diverse CT data, a multiply memory structure is presented to extend the capacity of constant error carousel (CEC) in the LSTM model. The experiment results indicate that the proposed model outperforms conventional models in the accuracy and stability for the short-term CTF. Further comparative experiments reveal that the 2-D architecture can enhance the prediction accuracy and the multi-CEC structure can improve the forecasting stability for the short-term CTF of wafer lots.

Bilateral LSTM: A Two-Dimensional Long Short-Term Memory Model With Multiply Memory Units for Short-Term Cycle Time Forecasting in Re-entrant Manufacturing Systems

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This paper surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, and criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Second, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, and data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances in industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this paper thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field, and the industrial, manufacturing, and automation field. This paper can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

Data Management in Industry 4.0: State of the Art and Open Challenges

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

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Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization

Recently, to ensure product quality in high-mix semiconductor manufacturing processes, run-to-run (R2R) control algorithms have received increasing attention. Difficult challenges have been met with new and advanced approaches that address the real-world operating conditions exhibited in high-mix production environments (e.g., different products with different frequencies and high numbers of threads). In this paper, various proposed controllers for high-mix semiconductor manufacturing processes are surveyed from an application and theoretical point of view. Remaining challenges and directions for future work are also summarized with the intent of drawing attention to these problems in the systems and process control communities.

Survey on Run-to-Run Control Algorithms in High-Mix Semiconductor Manufacturing Processes

Efficiency improvement of cogeneration system using statistical model

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Control System Teaching and Experiment Using LEGO MINDSTORMS NXT Robot

Affected by many factors, the photovoltaic output power is characterized by nonlinearity, volatility and instability. Therefore, short-term forecasting models are required to have multiple inputs, levels, and categories. In order to solve the above problems and improve the accuracy of predictions, this paper proposes a combined model prediction method based on similar-day clustering and the use of Conjugate Gradient (CG) to improve Deep Belief Network (DBN). The initial method uses fuzzy C-Means Clustering Algorithm (FCM) to perform similar-day clustering on the original data according to the degree of membership. The CG-DBN prediction model is then designed according to the category, with the model ultimately being used to perform the short-term prediction of the PV output power. The proposed scheme uses data from Zhejiang Longyou power station for experimental analysis and verification, and the results were compared with the back propagation neural networks model, Support Vector Machine (SVM) model, and traditional deep belief network. The model’s predicted results are compared. Finally, it is concluded that, in the short-term PV power load forecasting, the prediction performance of the FCM and CG-DBN combination forecast model is better than the above three models and has strong feasibility in short-term PV power forecasting.

Short Term Prediction of Photovoltaic Power Based on FCM and CG - DBN Combination

Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It’s planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.

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Zhengming Li

Papers

Survey on Run-to-Run Control Algorithms in High-Mix Semiconductor Manufacturing Processes

Efficiency improvement of cogeneration system using statistical model

Control System Teaching and Experiment Using LEGO MINDSTORMS NXT Robot

Short Term Prediction of Photovoltaic Power Based on FCM and CG - DBN Combination

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification