Log Operations Research Articles

Development of IoT Kaizen System for Smart Lean Raw Material Inventory Management: A case study at an SME factory in Malaysia

Effective Raw Material Inventory Management is crucial for overall company performance, demanding significant improvements. In the current technologically advanced landscape, the imperative adoption of the Internet of Things (IoT) is crucial for propelling inventory management forward and amplifying the visibility of raw materials. This case study is dedicated to developing an IoT Kaizen System, meticulously aligned with the Smart Lean Factory Inventory Control Framework, tailored to enhance Raw Material Inventory Management within an SME factory in Seremban, Negeri Sembilan. With a focal point on Loft Layer materials, the study encompasses Raw Material Ordering, Receiving, and Charging Out Log operations. Leveraging web-based tools such as Google Sheets, AppSheet, and Looker Studio, the system adeptly digitizes manual processes, tracking real-time data and integrating data visualization tools for informed decision-making. Implemented within a genuine industrial context, the project is intricately tailored to meet the unique preferences and requirements of the company, ensuring a customized solution that aligns with industry standards and reflects specific operational nuances. The resulting digital system introduces a structured, simplified, and efficient approach to Raw Material Inventory Management, injecting enhanced operational control and adaptability into the company’s workflow. Through the system implementation, warehouse space was reduced by 15%, order processing was done fully digitally that achieve zero case of ordering error and achieved zero case of material stock out. This system can adjust to what’s needed in modern manufacturing. It proves that using innovation and technology can make a business better by making things run more smoothly and helping with decision-making.

Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations

Abstract The rock acoustic data that provide important information about the formation petrophysics and geomechanics are highly needed to design the wells drilling programs, in addition to, reservoir stimulation and field development plans. The acoustic data acquisition through the conventional methods in the petroleum industry either by logging or lab measurements has many drawbacks as the cost of well log operations and the job time. Determining the acoustic data through the common correlations did not provide high accuracy, and there are limitations for using these correlations. The new trend in the petroleum industry with the fourth industrial revolution is to employ machine learning for such problems to provide effective solutions. Therefore, this study utilized the machine learning for developing prediction models for acoustic compressional and shear slowness using adaptive neuro-fuzzy inference system and support vector machines tools. The study presents novel contributions for predicting acoustic slowness from only the surface drilling data while drilling different formations of composite lithology (limestone, sandstone, shale, and carbonate). The study utilized real field data (2800 data points) to build and test the two models through deep sensitivity analysis, in addition, further testing for the models by another 2800 data points from the same field for the validation phase. The obtained results ensured the capability of machine learning for predicting the acoustic slowness with high accuracy as adaptive neuro-fuzzy inference system (ANFIS) achieved a correlation coefficient (R) higher than 0.98 and error less than 1.43% as average absolute percentage error (AAPE) between the actual and predicted acoustic values. Adaptive neuro-fuzzy inference model showed the highest accuracy during the model training as R was 1.0; in addition, R for testing results showed 0.98, and AAPE ranged from 0.25% to 0.92%. The validation phase ensured the high performance for the acoustic prediction of the developed models as R is higher than 0.98 and AAPE lower than 1.46%. The machine learning applications through the developed models for the acoustic data will provide cost and time savings for acoustic data acquisition for the field applications.

Skin and Reservoir Pressure Profiling Utilizing Production Logging

Production Log is a key log that directly define the facies and layers behavior. The PLT measures the inflow zone thickness and the flow rate of each phase which is directly function of facies properties in terms of deliverability. On the basis of pseudo steady state flow regime (which is the dominant phase of production during PLT log operations) a mathematical relationship developed to determine skin and reservoir pressure per each inflow zone with reasonable error.

Current Practices in Log Yard Design and Operations in the Province of Quebec, Canada

Abstract Log yards play an important role in the forest supply chain by connecting raw material supply to manufacturing processes. Log yard design and operations have not been thoroughly represented in the scientific literature, though research in other industrial sectors has demonstrated the strategic operational importance of warehouses. This article investigates the log yard design and operations in current industrial practices. An analysis of existing log yards was conducted in Quebec. Detailed information about throughput, equipment, personnel, inventory management, and design considerations was gathered by means of questionnaires, on-site visits, and meetings with yard managers. The survey of current practices (design, management, and operations) confirms that most existing yards have been designed without a systematic method. Crucial performance inhibitors included log yard shape, in-flow management, and poor surface material. Results point toward a potential gain in competitiveness by improving log yard practices, optimizing log yard shape and layout, better coordinating the forest–mill operations, and enhancing surface material.

Approaches on Primary Log Breakdown Process

Aim of study: Innovations of “primary breakdown process” which is the first step of log sawing process were evaluated with their reasons and results. Area of study: Primary log breakdown process were investigated with products of domestic and foreign log sawing machine manufacturers, scientific studies and operations at especially foreign sawmills. Material and Methods: Log quality assessment techniques, sawing techniques and sawing machines were compared with conventional methods. Main results: Losses during the log sawing can be minimized with small but effective innovations. However, domestic sawmills which are foreign-dependent on especially raw material (log) should consider their financial situations while taking an investment decision. Research highlights: Domestic sawmills can try “non-destructive evaluation techniques” for required evaluations while before, during and after the primary breakdown process.

A Two-Level Autonomous Intrusion Detection Model Inspired by the Immune System

There are several methods applied to distributed system security, which have the same absolute view of the intrusion. In their view, an operation could be legitimate or intrusive, which does not have any consistency with the complicated and heterogeneous nature of distributed systems. In this paper, a two level multi-agent model is proposed whose first level determines system’s unsafe behaviors based on anomaly occurrence. Then, its second level calculates the probability of system log operations effectiveness in the case of intrusion happens. If this probability is greater than the first-level prediction, the anomaly is known as intrusion, otherwise it is supposed as an unexpected legal behavior. Therefore, the false positive error probability will decrease. Also, the proposed multi-agent system utilizes the human immune system whose autonomous agents do not need maintenance and detects intrusions without relying on any other central elements, just by using their own learning and interaction capability..

The Application of Bluetooth in the Control System of the Smart Home with Internet of Things

Basing on the research of the network structure of the control system of the smart home and the characteristic of the wireless technology of the Bluetooth, it proposes an implementation of the control system of the smart home basing on the Bluetooth technology, builds the communication protocol architecture of the control subnet in smart home, and develops the smart home controller basing on the ARM9 S3C2440. In the control system of the smart home, it can implement the log, control, add, remove and other basic operations of the control sub net in the smart home system towards the household electrical appliances.

Gain Adapted Optimum Mixture Estimation Scheme for Single Channel Speech Separation

This paper presents the proof of an Optimum mixture estimator for the single channel speech separation problem, which is a technique for separating two speech signals from a single recording of their mixture. The presented work is an attempt to solve a fundamental limitation in the current single channel speech separation techniques, in which it is assumed that the data used in the training as well as test phases of the separation model have the same energy levels. To overcome this limitation, a gain adapted Optimum mixture estimator is derived, which estimates the mixture of speech signals under the different signal-to-signal ratios (SSRs). Specifically, the speakers’ gains are incorporated as unknown parameters into the separation model, and then the estimator is derived in terms of the source distributions and SSR. It is demonstrated that the use of the Optimum mixture estimator results in the lower estimation error than the non-linear mapping (log and inverse-log operations)-based Mixture-Maximization (MixMax) or Quadratic estimators. The experimental results based on the real speech data also depict that the proposed estimator improves the mixture estimation performance significantly when compared with MixMax or Quadratic estimators with the gain adaptation.

Efficient Implementation of a van der Waals Density Functional: Application to Double-Wall Carbon Nanotubes

We present an efficient implementation of the van der Waals density functional of Dion et al. [Phys. Rev. Lett. 92, 246401 (2004)], which expresses the nonlocal correlation energy as a double spatial integral. We factorize the integration kernel and use fast Fourier transforms to evaluate the self-consistent potential, total energy, and atomic forces, in O(NlogN) operations. The resulting overhead, for medium and large systems, is a small fraction of the total computational cost, representing a dramatic speedup over the O(N(2)) evaluation of the double integral. This opens the realm of first-principles simulations to the large systems of interest in soft matter and biomolecular problems. We apply the method to calculate the binding energies and the barriers for relative translation and rotation in double-wall carbon nanotubes.

On modeling and simulating transitions between microbial growth and inactivation or vice versa

Resumed growth of the survivors of a heat or chemical treatment after cooling or a disinfectant dissipation is not an uncommon phenomenon. Similarly, the inverse, the onset of mortality in a growing microbial population as a result of exposure to increasing temperature or concentration of an antimicrobial agent, is also a familiar scenario. Provided that in either regime, the organism has no time to adapt biologically, the continuous transition from growth to inactivation or vice versa can be simulated with conventional growth and inactivation models, whose rate constant is allowed to change sign. Where both the growth and inactivation follow first-order kinetics, the sign change has no effect on the model equation's solutions. The same applies when the growth and inactivation patterns are described by a rate model, like the differential logistic equation or its various variants. However, determination of such models' coefficients from experimental isothermal growth and inactivation data can be difficult for technical reasons, unless the model can be integrated analytically. If not, or when the model itself is unknown a priori, then the rate equation would have to be derived from the fit of empirical models like the Weibull, modified versions of the logistic function and the like. But this may create a new kind of problem as a result of that the log and certain power operations cannot be used for negative numbers. For certain models at least, the problem can be solved through modification of the procedure by which the rate equation is solved numerically. This is demonstrated in simulated transitions between growth and inactivation and between inactivation and growth based on the log linear and Weibullian-power law models and three logistic patterns based on a shifted logistic function, the Baranyi–Roberts model and a shifted arctan model.

Atomic broadcast in asynchronous crash-recovery distributed systems and its use in quorum-based replication

Atomic broadcast is a fundamental problem of distributed systems: It states that messages must be delivered in the same order to their destination processes. This paper describes a solution to this problem in asynchronous distributed systems in which processes can crash and recover. A consensus-based solution to atomic broadcast problem has been designed by Chandra and Toueg for asynchronous distributed systems where crashed processes do not recover. We extend this approach: it transforms any consensus protocol suited to the crash-recovery model into an atomic broadcast protocol suited to the same model. We show that atomic broadcast can be implemented requiring few additional log operations in excess of those required by the consensus. The paper also discusses how additional log operations can improve the protocol in terms of faster recovery and better throughput. To illustrate the use of the protocol, the paper also describes a solution to the replica management problem in asynchronous distributed systems in which processes can crash and recover. The proposed technique makes a bridge between established results on weighted voting and recent results on the consensus problem.

Multiplicative independence of algebraic numbers and expressions

A list of complex numbers is multiplicatively independent if no integral–exponent power product of them is equal to 1, unless all exponents are zero. A method of deciding multiplicative independence is given, for complex numbers in a finitely generated field, with given proper set of generators. This is based on computing an upper bound on absolute value for possible minimal non-zero integral exponents. As a consequence of this, a solution which does not use numerical approximation, depending on the Schanuel conjecture, can be given for the problem of deciding equality between two numbers given as closed-form expressions using exp, log , radicals, and field operations. It is argued, however, that an efficient solution of this problem is likely to use numerical approximation, together with an upper bound, depending on the syntax of the expressions for the numbers, for the amount of precision needed to distinguish the numbers if they are not the same. A conjecture is stated (the uniformity conjecture) which attempts to provide such an upper bound.

High speed on-line backup when using logical log operations

Media recovery protects a database from failures of the stable medium by maintaining an extra copy of the database, called the backup, and a media recovery log. When a failure occurs, the database is “restored” from the backup, and the media recovery log is used to roll forward the database to the desired time, usually the current time. Backup must be both fast and “on-line”, i.e. concurrent with on-going update activity. Conventional online backup sequentially copies from the stable database, almost independent of the database cache manager, but requires page-oriented log operations. But results of logical operations must be flushed to a stable database (a backup is a stable database) in a constrained order to guarantee recovery. This order is not naturally achieved for the backup by a cache manager concerned only with crash recovery. We describe a “full speed” backup, only loosely coupled to the cache manager, and hence similar to current online backups, but effective for general logical log operations. This requires additional logging of cached objects to guarantee media recoverability. We then show how logging can be greatly reduced when log operations have a constrained form which nonetheless provides very useful additional logging efficiency for database systems.

Logical logging to extend recovery to new domains

Recovery can be extended to new domains at reduced logging cost by exploiting “logical” log operations. During recovery, a logical log operation may read data values from any recoverable object, not solely from values on the log or from the updated object. Hence, we needn't log these values, a substantial saving. In [8], we developed a redo recovery theory that deals with general log operations and proved that the stable database remains recoverable when it is explained in terms of an installation graph. This graph was used to derived a write graph that determines a flush order for cached objects that ensures that the database remains recoverable. In this paper, we introduce a refined write graph that permits more flexible cache management that flushes smaller sets of objects. Using this write graph, we show how: (i) the cache manager can inject its own operations to break up atomic flush sets; and (ii) the recovery process can avoid redoing operations whose effects aren't needed by exploiting generalized recovery LSNs. These advances permit more cost-effective recovery for, e.g., files and applications.

Asymptotic behavior of solutions of a nonlinear differential equation of the first order

The asymptotic behavior at infinity of solutions of the equation u′ = P(u, t) Q(u, t) is studied. P, Q are polynomials in u whose coefficients are functions of t, and belong to the Hardy class H (i.e., to the set of all real-valued functions defined by finite many ordinary algebraic, exp, and log operations.) It is proved that, for any continuously differentiable solution u( t), there exists one or the other of the asymptotic formulae u( t) ~ h( t), In u( t) ~ h( t), within the class H , i.e., h(t) ϵ H . As the main tool for the proof it is first shown that any (real) solution y( t) of an algebraic equation whose coefficients are elements of H behaves at infinity again as an element of H .

Handling Logs by Electric Motocylinders

FOR YEARS sawmills have used various forms of specialized machinery to produce reciprocating and oscillating motions for log and timber-handling operations. Until recently this machinery was powered by steam (air) cylinder and piston, simple devices well suited to the applications, being fast, powerful, of low cost, and of rugged construction, an essential service requirement. Sawmill waste was used to generate the mill's total steam and power requirements.