Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

The Validity of the Thermohydrogravidynamic Theory Concerning the Predicted Dates of the Maximal Temporal Intensifications of the Global Seismotectonic Processes of the Earth during the Range 2020 - 2023 AD

We present (on the 13th International Conference on Geology and Geophysics) the convincing evidence that the strongest earthquakes (according to the U.S. Geological Survey) of the Earth (during the range 2020 - 2023 AD) occurred near the predicted (calculated in advance based on the global prediction thermohydrogravidynamic principles determining the maximal temporal intensifications of the global seismotectonic, volcanic, climatic and magnetic processes of the Earth) dates 2020.016666667 AD (Simonenko, 2020), 2021.1 AD (Simonenko, 2019, 2020), 2022.18333333 AD (Simonenko, 2021), 2023.26666666 AD (Simonenko, 2022) and 2020.55 AD, 2021.65 AD (Simonenko, 2019, 2021), 2022.716666666 AD (Simonenko, 2022), respectively, corresponding to the local maximal and to the local minimal, respectively, combined planetary and solar integral energy gravitational influences on the internal rigid core of the Earth. We present the short-term thermohydrogravidynamic technology (based on the generalized differential formulation of the first law of thermodynamics and the first global prediction thermohydrogravidynamic principle) for evaluation of the maximal magnitude of the strongest (during the March, 2023 AD) earthquake of the Earth occurred on March 16, 2023 AD (according to the U.S. Geological Survey). .

采前施用苯并噻二唑对‘蛇龙珠’葡萄果实发育过程中色泽和香气的影响

【目的】分析施用苯并噻二唑(benzothiadiazole,BTH)对葡萄成熟过程中叶绿素、类胡萝卜素、花色苷及相关基因表达量的影响,明确BTH对葡萄成熟过程中游离态和结合态香气种类和浓度的影响,初步探讨BTH调控葡萄果实花色苷和香气合成的机制。【方法】以酿酒葡萄‘蛇龙珠’为试材,果实膨大期整株喷施含0.1%吐温80的0.37 mmol·L^(-1)BTH水溶液,以仅喷施含0.1%吐温80的水溶液为对照组(CK)。通过测定葡萄果皮中的叶绿素、类胡萝卜素、总花色苷、总原花青素和花色苷合成相关基因表达量,表征游离态和结合态香气浓度和组成的变化,评估BTH处理对葡萄品质的影响。【结果】BTH处理显著降低葡萄成熟过程中叶绿素a、叶绿素b和总类胡萝卜素的积累,显著提高总原花青素、总花色苷和红葡萄颜色指数,促进葡萄着色。此外,BTH还显著上调VvCHS1、VvF3H1、VvDFR、VvLDOX、VvUFGT和VvMYBA1等花色苷合成相关基因的表达量。BTH处理显著降低游离态和结合态C_(6)/C_(9)化合物、游离态直链脂肪族、游离态支链脂肪族含量,显著提高结合态直链脂肪族、游离态和结合态芳香族、结合态支链脂肪族、游离态和结合态萜烯、游离态和结合态降异戊二烯浓度。通过定义葡萄成熟阶段的定性标记物和定量标记物发现,成熟过程中两个相邻发育期葡萄中香气物质的演变差异较大,BTH能显著影响‘蛇龙珠’葡萄成熟过程中的游离态和结合态香气演变趋势,BTH处理能显著改变花后9周和11周的游离态以及11周的结合态香气积累趋势。此外,游离态C_(6)/C_(9)化合物、直链脂肪族和萜烯,结合态C_(6)/C_(9)化合物、直链脂肪族、萜烯六大类香气是区分CK和BTH葡萄呈香表现的主要贡献组分。以气味活性值>0.1为阈值,共筛选出包括3-己烯醛和β-紫罗兰酮在内的23个活性香气成分。BTH处理主要减弱葡萄的植物香和脂肪香,增强花卉香和水果香气味。【结论】BTH通过改变叶绿素、类胡萝卜素、花色苷和合成相关基因的表达模式,促进葡萄着色,改变香气含量和组成,进而提高葡萄品质。

响应面法优化苦荞鲜湿面工艺研究

以挤压熟化苦荞粉和小麦粉为原料,以吸水率和蒸煮损失率为指标,分别考察苦荞粉添加量、醒发时间、加水量、水温、和面时间对苦荞鲜湿面品质的影响。在单因素试验的基础上,通过响应面法优化苦荞鲜湿面的加工工艺。结果表明:苦荞鲜湿面的最佳工艺为以小麦与苦荞混合粉质量为基准,苦荞粉添加量51%、醒发时间20 min、加水量101%。在此条件下制得的苦荞鲜湿面吸水率为52.66%±0.75%、蒸煮损失率为1.00%±0.06%,口感爽滑黏弹,具有苦荞特有的香气,感官评价接近小麦鲜面条。

Liquid-mediated Ostwald ripening of Ag-based clusters supported on oxides

Reconstruction of supported nanocatalysts often occurs in gas-solid reactions and significantly affects the catalytic performance,yet it is much less explored in liquid-phase environment.Herein,we find that highly-dispersed Ag nanocatalysts,i.e.,AgOx clusters,supported on alumina,silica,and titania,can aggregate into larger Ag or Ag2O particles after immersing in liquid-phase media at room temperature.The spontaneous aggregation of AgOx clusters in liquid water is attributed to liquid-phase Ostwald ripening through dissolution of AgOx clusters into water and subsequent redeposition to form Ag2O particles.The immersion into organic solvents such as ethanol leads to reduction of AgOx clusters and further growth into Ag particles.This work reveals that liquid-phase reaction media can induce substantial structural evolution of supported nanostructured catalysts,which should be carefully considered in liquid-solid interface catalytic reactions such as electrocatalysis,environmental catalysis,and organic synthesis in liquid phase.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Evolution of plasticized MnO–Al2O3–SiO2-based nonmetallic inclusion in 18wt%Cr-8wt% Ni stainless steel and its properties during soaking process

The properties of MnO–Al2O3–SiO2-based plasticized inclusion are likely to change during soaking process due to its low melting point. In this study, the evolution of the MnO–Al2O3–SiO2-based inclusion of 18 wt%Cr-8 wt%Ni stainless steel under isothermal soaking process at 1250°C for different times was investigated by laboratory-scale experiments and thermodynamic analysis. The results showed that the inclusion population density increased at the first stage and then decreased while their average size first decreased and then increased. In addition, almost no Cr2O3-concentrated regions existed within the inclusion before soaking, but more and more Cr2O3 precipitates were formed during soaking. Furthermore, the plasticity of the inclusion deteriorated due to a decrease in the amount of liquid phase and an increase in the high-melting-pointphase MnO–Cr2O3 spinel after the soaking process. In-situ observations by high-temperature confocal laser scanning microscopy(CLSM) confirmed that liquid phases were produced in the inclusions and the inclusions grew rather quickly during the soaking process. Both the experimental results and thermodynamic analysis conclude that there are three routes for inclusion evolution during the soaking process. In particular, Ostwald ripening plays an important role in the inclusion evolution, i.e., MnO–Al2O3–SiO2-based inclusions grow by absorbing the newly precipitated smaller-size MnO–Cr2O3 inclusions.

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be improved.Widely used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding performance.Hence,combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance.A novel vibration recognition method based on signal processing and deep neural networks is proposed herein.First,environmental vibration signals are collected;then,signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms:the wavelet transform,Hilbert-Huang transform,and Mel frequency cepstral coefficient extraction method.Subsequently,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain characteristics.Finally,the performance of the trained deep neural networks is evaluated and validated.The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.

Short-term Memory and Listening Comprehension

More and more attention is paid to listening comprehension and learner factors nowadays, but less attention is paid to the effect of short-term memory. By analyzing the function and effect of short-term memory in the process of information in the mind, this essay points out that short-term memory plays a vital role in listening comprehension, and puts forward three most effective ways to improve short-term memory retention.

后熟过程对牡蛎风味及营养物质的影响

牡蛎是重要的海水经济养殖动物,因营养丰富、产量高而深受消费者喜爱。该研究通过对比生鲜牡蛎肉及经后熟过程后的牡蛎肉,在美拉德反应后的感官分析评价及营养物质、风味物质分析,获得综合评价。研究结果显示,从感官评分来看,后熟处理后的牡蛎气味评分可达到14.6±0.84,显著高于新鲜牡蛎产品(P<0.05);而这一醇厚气味的产生是后熟过程使牡蛎体内原有的醛类、酯类等独特香气挥发性物质含量显著增高所致(P<0.05);从营养成分来看,经过后熟处理,牡蛎的氨基酸总量、必需氨基酸占比等均高于新鲜牡蛎,分别达到16.18 g/100 g和36.4%,饱和脂肪酸和不饱和脂肪酸也有提高。研究结果表明,后熟工艺使牡蛎产品的营养价值、气味及口感均得到极大提升,该研究可为牡蛎新产品的开发提供重要理论指导和技术支持。

原料熟化工艺对油莎豆酱品质及风味影响

以油莎豆为原料,米曲霉为发酵菌种,采用常压蒸煮工艺(M1)、高压蒸煮工艺(M2)和挤压膨化工艺(M3)对原料进行预处理,探讨3种不同熟化工艺对酿造豆酱中氨基酸态氮含量、总酸含量、还原糖含量、色泽、感官评分、有机酸含量、氨基酸含量、挥发性化合物等指标的影响。研究结果表明,在品质上,与M1和M3相比,M2样品中氨基酸态氮含量、总酸含量、还原糖含量和色差值均高于其他组。在风味上,与M1和M3相比,M2样品中有机酸含量相对较高,但M3样品中游离氨基酸的含量显著高于M1和M2。GC-MS检测出M1、M2和M3的挥发性化合物分别有28,16,26种,但其挥发性化合物的总含量比较接近。整体上看,高压蒸煮工艺是更适合油莎豆酱进行熟化的一种方式。

奶酪发酵剂红曲霉的筛选及其对奶酪理化、质构和抗氧化性的影响

为筛选适宜用作奶酪发酵剂的红曲霉菌株,通过对传统红曲发酵食品进行增菌培养,采用平板划线法初筛。将初筛菌株以一种新型短期成熟工艺制备奶酪进行风味品评复筛,最终筛选出6株疑似红曲霉的风味优势菌,通过DNA序列比对,确认为红曲霉。将6株优势菌以短熟工艺制备奶酪样品,并进行安全性、功能性、理化指标、质构和抗氧化性的检测,分析其对奶酪品质的影响。结果表明,红曲霉菌株BC20及ZX-99同时具备食用安全性及奶酪熟化能力。在奶酪成熟结束时,奶酪样品中的桔霉素含量皆低于国标检测限25μg/kg,远低于定量限80μg/kg,pH 4.6可溶性氮含量分别为(40.99±0.90)%、(26.05±0.56)%。该研究筛选出2株适宜制作成熟奶酪的红曲霉菌株,提供了一种制备红曲霉奶酪的新型短熟工艺,为红曲霉奶酪品质的优化及缩短成熟期、减少生产成本提供依据。

福建6个香牙蕉品种(品系)后熟过程中挥发性风味成分的变化

采用顶空-气相色谱-质谱联用技术(HS-GC-MS/MS)鉴定福建省6个香牙蕉品种(品系)在不同后熟时间的挥发性风味成分,结合主成分分析(PCA)法和相对气味活度值(ROAV)法评价香牙蕉的香气特征。结果表明:6个香牙蕉品种(品系)在后熟过程中共检测到36种挥发性风味成分;成熟度对挥发性风味成分的种类和相对含量均有显著影响;随着成熟度升高,醛类成分相对含量先升高后持续下降,酯类成分在后熟第7 d时检出并持续升高,后熟至第10 d时为86.02%~92.32%;(E)-2-壬烯醛、反-2-,顺-6-壬二烯醛、正己醛、2-正戊基呋喃、反-2-己烯醛是青香蕉中的关键香气成分,丁酸异戊酯、1-甲基乙酸丁酯、乙酸异戊酯、乙酸异丁酯是黄香蕉中的关键香气成分;醇类、酮类成分相对含量较低且对香牙蕉整体香气无影响。探析福建省香牙蕉挥发性风味成分特点,补充了青香蕉风味成分数据库,为香牙蕉育种、贮藏保鲜及加工利用提供参考。

不同加工工艺对预调理乳鸽感官品质及理化特性的影响

该研究以新鲜乳鸽为原材料,经过腌制、低温风干、强化高温成熟、预煮、烤制等加工处理制作预调理乳鸽产品。研究了风干时间、强化高温成熟时间、预煮时间、烤制时间等条件对乳鸽产品感官品质的影响,通过正交试验优化乳鸽产品加工工艺,并分析不同加工工艺对产品品质及理化指标的影响。结果表明,4种加工工艺在乳鸽加工过程中均对产品感官品质有显著影响,且强化高温成熟对于乳鸽的感官品质有重要贡献。乳鸽产品游离氨基酸总量受强化成熟温度影响显著,强化高温处理后的样品游离氨基酸总量显著高于对照组(P<0.05),且主要鲜味氨基酸和人体必需氨基酸也显著高于对照组(P<0.05)。通过正交试验优化后发现,当低温风干时间为12 h,强化高温成熟时间为0.5 h,预煮时间为15 min,烤制时间为25 min时,加工的乳鸽感官品质相对较好,肉质细嫩多汁,五香味浓郁且表皮有光泽,色泽饱满红亮,表皮口感酥脆,鲜香可口,回味悠长,感官评分最高。

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

锂离子动力电池电解质六氟磷酸锂熟成工艺技术研究

针对LiPF_(6)粗产品不溶物含量较高的问题,以LiPF_(6)为研究对象,对在不同压力条件下采用高纯PF5气体对LiPF_(6)粗产品进行熟成处理的工艺技术进行了研究。考察了在不同压力条件下,LiPF_(6)粗产品中不溶物的变化情况。结果表明,在相同压力下,不溶物含量逐渐降低最后趋于某一特定值保持不变;在不同压力条件下,随着压力的增大,不溶物降低至100ppmw所需的时间逐渐减少。本研究成果为合理调节LiPF_(6)生产体系中的不溶物含量提供了理论依据。

外源草酸对采后番荔枝后熟及耐藏性的影响

研究外源草酸对采后番荔枝后熟软化及保鲜效果的影响,为番荔枝贮藏保鲜提供新方法。以AP番荔枝为试材,采用5mmol/L草酸溶液浸泡果实20min并在15℃条件下贮藏,研究草酸处理对果实的后熟软化、开裂及相关生理指标的影响。结果表明:草酸处理有效延缓了番荔枝果实硬度的下降和可溶性固形物含量的上升,抑制了果实的开裂及果胶酯酶(PE)、多聚半乳糖醛酸酶(PG)酶活性;草酸处理还能提高果实贮藏后期超氧化物歧化酶、过氧物酶的活性,抑制果实可溶性蛋白质和VC含量的下降;同时,草酸处理也抑制了果实中多酚氧化酶活性的升高和MDA含量的积累。相关性分析表明PE活性、PG活性与果实软化和开裂密切相关。外源草酸能有效地抑制采后番荔枝的后熟衰老进程,有较好的保鲜效果。

金华火腿发酵成熟现代工艺及装备研究

跟踪金华火腿传统工艺方法，研究火腿发酵成熟现代工艺、研制工艺装备；发酵成熟现代工艺保持“低温脱水、中温发酵、高温成熟”传统工艺特色，按照“强化高温成熟、缩短工艺时间”原则正交试验，并优化现代工艺参数。结果表明：现代发酵成熟45～80d比传统工艺时间缩短55．5％～70％；火腿脱水率和工艺时间与温、湿度密切相关，80d火腿脱水率（29．43％±1．16％）已超过传统工艺水平，风味达到传统一级火腿水平，且火腿肉面没有霉菌斑点；现代发酵成熟工艺装备能实现温湿度和进风量等工作参数的自动控制，以火腿脱水率为目标函数进行的现代工艺回归优化结果与火腿感官品质评定结果有良好的一致性。

外源草酸延缓采后芒果成熟及其生理基础的研究

【目的】研究外源草酸对采后芒果(Mangifera indica L.)的保鲜效应,为芒果贮藏保鲜提供新方法。【方法】芒果栽培种'圣心'采后经5mmol·L-1草酸溶液浸果10min后在常温(25℃)下贮藏,研究草酸处理对果实的成熟进程及其相关生理指标的影响。【结果】草酸处理后在贮藏期间果实的软化、可溶性固形物(SSC)增加、可滴定酸(TA)降低和果皮细胞的电解质渗漏量增加的速率都显著低于未处理的果实;处理果实果皮的超氧歧化酶(SOD)活性在采后6～15d提高,脂氧合酶(LOX)活性在6、9d时降低,同时,超氧阴离子(O2-·)在采后贮藏的前12d和过氧化氢(H2O2)在9、12d时含量分别降低;13d后处理果实的乙烯释放量显著降低;贮藏结束时(18d),草酸处理果实的表皮红色着色好于对照,腐烂率显著低于对照,果肉口感没有产生变异。【结论】外源草酸有效地延缓采后芒果的成熟进程;草酸提高果皮的SOD活性和降低LOX活性从而导致活性自由基(ROS)含量下降,以及降低果实的乙烯释放量等生理效应与延缓果实成熟密切相关,同时有助于果实的抗病性;草酸处理是采后芒果贮藏保鲜的一种可选新方法。