This paper focuses on the ANN approach for Note recognition by frequency and amplitude involves extracting relevant acoustic features. Precedent data is used to design a solution for recognizing melody using a neural ...
详细信息
Standard method of hazard identification HAZOP has several limitations: impossibility to identify hazards involving interactions between different parts of complex system;experts’ experience can fail while evaluating...
详细信息
This study investigated the most influential composting parameters (temperature, pH, humidity, C/N ratio, and CO2 emissions) on final compost quality. Composting bags (turned daily) containing prunings, lignocellulose...
详细信息
This study investigated the most influential composting parameters (temperature, pH, humidity, C/N ratio, and CO2 emissions) on final compost quality. Composting bags (turned daily) containing prunings, lignocellulose material, cattle manure, and household organic waste were monitored for eight weeks across three batches. All the temperature data registered daily were statistically analyzed with one-way ANOVA to verify the consistency between batches. This study employs a first-order kinetic model to quantify the decomposition of organic matter in composting, revealing the influence of temperature on the decomposition rate. Principal component analysis was conducted for these factors and their interactions with respect to the final compost quality. Finished compost showed a significantly high nitrogen, phosphorus, and potassium content compared with that in the feedstock of the initial compost mixture, while there was constant organic carbon content. In addition to the control of key parameters, a linear regression analysis allowed us to identify the factors that most influenced temperature. We identified two key models: one including pH, humidity, C/N ratio, and CO2 emissions, and another focusing only on CO2. This study emphasized the importance of monitoring and controlling the crucial parameters of the composting process in order to obtain quality compost suitable for application in soil use. Moreover, high temperatures possibly attained in the process of aerobic decomposition destroy the pathogens, making the end products safer for uses in agriculture.
In distributed photovoltaic (PV) power generation systems, data quality plays a critical role in the accuracy of predictive models. However, the complexity of distributed PV sensor data, including issues such as missi...
详细信息
Airworthiness Examination is a kind of verification activity for the results of model development, whose purpose is to ensure that the developed model conforms to its original design;Airworthiness of military aircraft...
详细信息
ISBN:
(数字)9781510650398
ISBN:
(纸本)9781510650398;9781510650381
Airworthiness Examination is a kind of verification activity for the results of model development, whose purpose is to ensure that the developed model conforms to its original design;Airworthiness of military aircraft is developed on the basis of Airworthiness of civil aircraft in order to improve the safety of military aircraft. Firstly, in this paper, the Airworthiness Examination process of military and civil aircraft is established. From the perspective of requirements difference, key steps, number of nodes, work content and examination time. The comparative elements of multi-dimensional Airworthiness Examination process of military and civil aircraft are extracted by using structural entropy theory. Secondly, the comparative analysis model of structural entropy is constructed, and the key data such as system time effectiveness entropy and structural entropy corresponding to Airworthiness Examination process of military and civil aircraft are calculated. Finally, this paper puts forward the corresponding measures and suggestions for the Airworthiness Examination process of military aircraft, which has certain practical significance in technical methods and application practice.
Patients suffering from tremors have difficulty performing activities of daily living. The development of a model of a limb with tremors can pave the way for nonsurgical tremor suppression control techniques. Neverthe...
详细信息
ISBN:
(纸本)9798350328066
Patients suffering from tremors have difficulty performing activities of daily living. The development of a model of a limb with tremors can pave the way for nonsurgical tremor suppression control techniques. Nevertheless, nonlinearity and actuator saturation make it difficult to develop an accurate model and a tremor suppression control method. Towards addressing this issue, this paper describes a Koopman-based method for system identification and its application to the design of a model predictive control (MPC) scheme to suppress tremors. Since model prediction accuracy is critical to the performance of an MPC, it is essential to update the model online if the predictions are not sufficiently accurate. We propose a recursive least squares (RLS) algorithm to improve control performance with low computational complexity. Finally, for the first time, stability analysis and recursive feasibility of the Koopman-based MPC (KMPC) closed-loop updated system are presented. The proposed modeling and control approach have been validated by experimental data and simulation results.
The paper presents a comprehensive application of stepwise weighting ratio analysis (SWARA), combined tradeoff solution (CoCoSo) and neural network methods to determine the optimal parametric combinations of mechanoac...
详细信息
The study proposes a way of developing granular models based on optimized subsets of data with different sampling sizes, in which three generally used models, namely Support Vector Machine, K-Nearest Neighbor, and Lon...
详细信息
The study proposes a way of developing granular models based on optimized subsets of data with different sampling sizes, in which three generally used models, namely Support Vector Machine, K-Nearest Neighbor, and Long Short-Term Memory, are designed and transformed into granular version for achieving a good performance with sufficient functionality. First, a collection of subsets are determined using different sampling methods, which are subsequently applied to play as an essential prerequisite of the proposed models. Then, the principle of justifiable granularity is utilized to the design of interval information granules based on the subsets of data. The design process is associated with a well-defined optimization problem realized by achieving a sound compromise between two conflicting criteria: coverage and specificity. To evaluate the performance of the granular models, two aspects are considered: (i) sampling methods used in determining suitable subsets of data;(ii) different models applied to be transformed into granular models. A series of experimental studies are conducted to verify the feasibility of the proposed granular models.
In this paper, we discuss how process mining techniques can be applied in industrial control systems for modeling, verification, and enhancement of the cyber-physical system based on recorded data logs. process mining...
详细信息
ISBN:
(数字)9781728175683
ISBN:
(纸本)9781728175683
In this paper, we discuss how process mining techniques can be applied in industrial control systems for modeling, verification, and enhancement of the cyber-physical system based on recorded data logs. process mining is used for extracting the process models in different notations from the recorded behavioral traces of the system. The output model of the system's behavior is mainly derived using an open-source tool called ProM. The model can be used for such applications as anomaly detection, detection of cyber-attacks and alarm analysis in industrial control systems with the help of various control flow discovery algorithms. The extracted process model can be used to verify how the event log deviates from it by replaying the log on Petri net for conformance analysis.
This paper tackles the challenge of effectively regulating furnace temperature in the municipal solid waste incineration (MSWI) process. It proposes an approach that integrates reinforcement learning with traditional ...
详细信息
ISBN:
(纸本)9798350387780;9798350387797
This paper tackles the challenge of effectively regulating furnace temperature in the municipal solid waste incineration (MSWI) process. It proposes an approach that integrates reinforcement learning with traditional PID control. Firstly, an analysis of the characteristics of furnace temperature control is conducted to identify the key manipulated variables affecting it. Subsequently, a control strategy specifically tailored for furnace temperature is developed, employing online adaptive PID with a BPNN-fitted actor-critic network (BPNN-ACN-PID). Finally, the effectiveness of the proposed method is validated through control experiments using actual processdata from a MSWI plant in Beijing.
暂无评论