An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double IA Harmony,and Causal Computation

Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that are turned into Go like problems with help of a feature enrichment technique.Second,reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction,uncertainty propagation,and path or tree searching.Particularly,causal potential theory is proposed for discovering causal direction,with two roads developed for its implementation.

Optimal Guidance Law to Maximize Terminal Velocity for Missiles with Impact Angle Constraint

In this paper,an optimal guidance law for missiles with impact angle and miss distance constraints is proposed to achieve the maximal terminal velocity. The normal acceleration command that includes the timevarying coefficients is introduced to satisfy the desired impact angle as well as zero miss distance according to the geometric relation and relative motion parameters between missile and target. The problem is formulated as an optimal control problem by defining the angle of velocity error and flight-path angle as state variables and maximizing a performance index of the terminal velocity. The analytical form of the proposed guidance law is obtained as the solution of the optimal control problem combining optimal control theory and numerical value computation method. Nonlinear simulations of various situations demonstrate the performance and feasibility of the proposed optimal guidance law.

Models for Ordering Multiple Products Subject to Multiple Constraints, Quantity and Freight Discounts

One of the most important responsibilities of a supply chain manager is to decide “how much” (or “many”) of inventory items to order and how to transport them. This paper presents four mixed-integer linear programming models to help supply chain managers make these decisions for multiple products subject to multiple constraints when suppliers offer quantity discounts and shippers offer freight discounts. Each model deals with one of the possible combinations of all-units, incremental quantity discounts, all-weight and incremental freight discounts. The models are based on a piecewise linear approximation of the number of orders function. They allow any number of linear constraints and determine if independent or common (fixed) cycle ordering has a lower total cost. Results of computational experiments on an example problem are also presented.

An intelligent automatic correlation method of oilbearing strata based on pattern constraints:An example of accretionary stratigraphy of Shishen 100 block in Shinan Oilfield of Bohai Bay Basin,East China

Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.

Clostridium difficile细胞毒素B羧基末端功能区的克隆与表达

目的:克隆并表达Clostridiumdifficile(Cdifficile)胞毒素B羧基末端功能区基因,为探索高效的防治Cdifficile感染的疫苗和诊断抗原奠定基础.方法:提取Cdifficile染色体基因,用PCR方法扩增ToxinB3基因,将其克隆至表达载体PET22b(+),用重组质粒转化大肠杆菌[E.coliBL21(DE3)],并在大肠杆菌BL21(DE3)中表达.用SDS-PAGE方法对表达产物进行分析.结果:从Cdifficile基因组DNA中成功地克隆了毒素B的羧基末端重复区域的1848bp基因,经过双酶切、PCR和测序鉴定分析,插入到载体的基因与GenBank中公布的CdifficileVPI10463的ToxinB3基因序列的同源性为99%.SDS-PAGE显示,目的基因表达产物的分子质量为71.3ku,利用表达载体PET22b(+)表达出蛋白质,重组蛋白表达量占菌体总蛋白的34.8%.结论:含CdifficileToxinB3基因的PET22b(+)重组质粒能高效表达目的基因.该重组子的构建为Clostridiumdifficile相关性疾病的诊断及后期制备疫苗,提供了有力的保障.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

Precipitation Retrieval from Himawari-8 Satellite Infrared Data Based on Dictionary Learning Method and Regular Term Constraint

In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.

Modified constraint-induced movement therapy alters synaptic plasticity of rat contralateral hippocampus following middle cerebral artery occlusion

Modified constraint-induced movement therapy is an effective treatment for neurological and motor impairments in patients with stroke by increasing the use of their affected limb and limiting the contralateral limb.However,the molecular mechanism underlying its efficacy remains unclear.In this study,a middle cerebral artery occlusion(MCAO)rat model was produced by the suture method.Rats received modified constraint-induced movement therapy 1 hour a day for 14 consecutive days,starting from the 7^th day after middle cerebral artery occlusion.Day 1 of treatment lasted for 10 minutes at 2r/min,day 2 for 20 minutes at 2 r/min,and from day 3 onward for 20 minutes at 4 r/min.CatWalk gait analysis,adhesive removal test,and Y-maze test were used to investigate motor function,sensory function as well as cognitive function in rodent animals from the 1st day before MCAO to the 21^st day after MCAO.On the 21^st day after MCAO,the neurotransmitter receptor-related genes from both contralateral and ipsilateral hippocampi were tested by micro-array and then verified by western blot assay.The glutamate related receptor was shown by transmission electron microscopy and the glutamate content was determined by high-performance liquid chromatography.The results of behavior tests showed that modified constraint-induced movement therapy promoted motor and sensory functional recovery in the middle cerebral artery-occluded rats,but had no effect on cognitive function.The modified constraint-induced movement therapy upregulated the expression of glutamate ionotropic receptor AMPA type subunit 3(Gria3)in the hippocampus and downregulated the expression of the beta3-adrenergic receptor gene Adrb3 and arginine vasopressin receptor 1 A,Avprla in the middle cerebral artery-occluded rats.In the ipsilateral hippocampus,only Adra2 a was downregulated,and there was no significant change in Gria3.Transmission electron microscopy revealed a denser distribution the more distribution of postsynaptic glutamate receptor 2/3,which is an a-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor,within 240 nm of the postsynaptic density in the contralateral cornu ammonis 3 region.The size and distribution of the synaptic vesicles within 100 nm of the presynaptic active zone were unchanged.Western blot analysis showed that modified constraint-induced movement therapy also increased the expression of glutamate receptor 2/3 and brain-derived neurotrophic factor in the hippocampus of rats with middle cerebral artery occlusion,but had no effect on Synapsin I levels.Besides,we also found modified constraint-induced movement therapy effectively reduced glutamate content in the contralateral hippocampus.This study demonstrated that modified constraint-induced movement therapy is an effective rehabilitation therapy in middle cerebral artery-occluded rats,and suggests that these positive effects occur via the upregulation of the postsynaptic membrane a-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor expression.This study was approved by the Institutional Animal Care and Use Committee of Fudan University,China(approval No.201802173 S)on March 3,2018.

Integrated guidance and control of guided projectile with multiple constraints based on fuzzy adaptive and dynamic surface

Based on fuzzy adaptive and dynamic surface(FADS),an integrated guidance and control(IGC)approach was proposed for large caliber naval gun guided projectile,which was robust to target maneuver,canard dynamic characteristics,and multiple constraints,such as impact angle,limited measurement of line of sight(LOS)angle rate and nonlinear saturation of canard deflection.Initially,a strict feedback cascade model of IGC in longitudinal plane was established,and extended state observer(ESO)was designed to estimate LOS angle rate and uncertain disturbances with unknown boundary inside and outside of system,including aerodynamic parameters perturbation,target maneuver and model errors.Secondly,aiming at zeroing LOS angle tracking error and LOS angle rate in finite time,a nonsingular terminal sliding mode(NTSM)was designed with adaptive exponential reaching law.Furthermore,combining with dynamic surface,which prevented the complex differential of virtual control laws,the fuzzy adaptive systems were designed to approximate observation errors of uncertain disturbances and to reduce chatter of control law.Finally,the adaptive Nussbaum gain function was introduced to compensate nonlinear saturation of canard deflection.The LOS angle tracking error and LOS angle rate were convergent in finite time and whole system states were uniform ultimately bounded,rigorously proven by Lyapunov stability theory.Hardware-in-the-loop simulation(HILS)and digital simulation experiments both showed FADS provided guided projectile with good guidance performance while striking targets with different maneuvering forms.

Theory of Flexural Shear, Bending and Torsion for a Thin-Walled Beam of Open Section

Aspects of the general Vlasov theory are examined separately as applied to a thin-walled channel section cantilever beam under free-end end loading. In particular, the flexural bending and shear that arise under transverse shear and axial torsional loading are each considered theoretically. These analyses involve the location of the shear centre at which transverse shear forces when applied do not produce torsion. This centre, when taken to be coincident with the centre of twist implies an equivalent reciprocal behaviour. That is, an axial torsion applied concentric with the shear centre will twist but not bend the beam. The respective bending and shear stress conversions are derived for each action applied to three aluminium alloy extruded channel sections mounted as cantilevers with a horizontal principal axis of symmetry. Bending and shear are considered more generally for other thin-walled sections when the transverse loading axes at the shear centre are not parallel to the section = s centroidal axes of principal second moments of area. The fixing at one end of the cantilever modifies the St Venant free angular twist and the free warping displacement. It is shown from the Wagner-Kappus torsion theory how the end constrained warping generates an axial stress distribution that varies with the length and across the cross-section for an axial torsion applied to the shear centre. It should be mentioned here for wider applications and validation of the Vlasov theory that attendant papers are to consider in detail bending and torsional loadings applied to other axes through each of the centroid and the web centre. Therein, both bending and twisting arise from transverse shear and axial torsion applied to each position being displaced from the shear centre. Here, the influence of the axis position upon the net axial and shear stress distributions is to be established. That is, the net axial stress from axial torsional loading is identified with the sum of axial stress due to bending and axial stress arising from constrained warping displacements at the fixing. The net shear stress distribution overlays the distributions from axial torsion and that from flexural shear under transverse loading. Both arise when transverse forces are displaced from the shear centre.

Effective Hybrid Teaching-learning-based Optimization Algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Adaptive weighting impact angle optimal guidance law considering seeker's FOV angle constraints

In this paper, a new adaptive optimal guidance law with impact angle and seeker’s field-of-view(FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable impact angle weighting(IAW) coefficient is introduced and used to modify the guidance law to make it adaptive for all guidance constraints. After integrating the closed-form solution of the guidance command with linearized engagement kinematics, the analytic predictive models of impact angle and FOV angle are built, and the available range of IAW corresponding to constraints is certain. Next, a calculation scheme is presented to acquire the real-time value of IAW during the entire guidance process. When applying the proposed guidance law, the IAW will keep small to avoid a trajectory climbing up to limit FOV angle at an initial time but will increase with the closing target to improve impact position and angle accuracy, thereby ensuring that the guidance law can juggle orders of guidance accuracy and constraints control.

An impact angle constraint integral sliding mode guidance law for maneuvering targets interception

An integral sliding mode guidance law(ISMGL)combined with the advantages of the integral sliding mode control(SMC)method is designed to address maneuvering target interception problems with impact angle constraints.The relative motion equation of the missile and the target considering the impact angle constraint is established in the longitudinal plane,and an integral sliding mode surface is constructed.The proposed guidance law resolves the existence of a steady-state error problem in the traditional SMC.Such a guidance law ensures that the missile hits the target with an ideal impact angle in finite time and the missile is kept highly robust throughout the interception process.By adopting the dynamic surface control method,the ISMGL is designed considering the impact angle constraints and the autopilot dynamic characteristics.According to the Lyapunov stability theorem,all states of the closed-loop system are finally proven to be uniformly bounded.Simulation results are compared with the general sliding mode guidance law and the trajectory shaping guidance law,and the findings verify the effectiveness and superiority of the ISMGL.

Multiple constraints-based QoS multicast routing: model and algorithms

Constraint-based multicast routing, which aims at identifying a path that satisfies a set of quality of service (QoS) constraints, has became a very important research issue in the areas of networks and distributed systems. In general, multi-constrained path selection with or without optimization is a NP-complete problem that can not be exactly solved in polynomial time. Hence, accurate constraints-based routing algorithms with a fast running time are scarce, perhaps even non-existent. The expected impact of such a constrained-based routing algorithm has resulted in the proposal of numerous heuristics and a few exact QoS algorithms. This paper aims to give a thorough, concise and fair evaluation of the most important multiple constraint-based QoS multicast routing algorithms known today, and it provides a descriptive overview and simulation results of these multi-constrained routing algorithms.

Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints

In this paper,guaranteed cost attitude tracking con-trol for uncertain quadrotor unmanned aerial vehicle(QUAV)under safety constraints is studied.First,an augmented system is constructed by the tracking error system and reference system.This transformation aims to convert the tracking control prob-lem into a stabilization control problem.Then,control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances,and they are incorporated into the reward function as penalty items.Based on the modified reward function,the problem is simplified as the optimal regulation problem of the nominal augmented system,and a new Hamilton-Jacobi-Bellman equation is developed.Finally,critic-only rein-forcement learning algorithm with a concurrent learning tech-nique is employed to solve the Hamilton-Jacobi-Bellman equa-tion and obtain the optimal controller.The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances,but also enforce safety constraints.The performance of the algorithm is evaluated by the numerical simulation.

Research on the navigation method of large-scale differential tail-control improvised guided munitions based on rotational speed constraints

In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Secure symbol level precoding for cell-free network based on band-region constraint of the eavesdropper’s receiving signal

A symbol level secure precoding scheme based on band-region constraint of the eavesdropper’s receiving signal is proposed to enhance the energy efficiency of cell-free multiple-input multiple-output(MIMO)networks in the presence of an eavesdropper while guaranteeing the quality of service(QoS)of user and the security of system.Moreover,to lighten its high computational complexity,original problem is divided into several cascade sub-problems firstly,and then those sub-problems are handled by combining Lagrangian dual function and improved Hooke-Jeeves method together.Comparative ex-periment with other secure symbol-level precoding schemes demonstrate that proposed scheme can achieve the lower power consumption with almost same symbol error rate and QoS of user.

Judgement of Design Scheme Based on Flexible Constraint in ICAD

The conception of flexible constraint is proposed in the paper. The solution of flexible constraint is in special range, and maybe different in different instances of same design scheme. The paper emphasis on how to evaluate and optimize a design scheme with flexible constraints based on the satisfaction degree function defined on flexible constraints. The conception of flexible constraint is used to solve constraint conflict and design optimization in complicated constraint based assembly design by the PFM parametrization assembly design system. An instance of gear box design is used for verifying optimization method.